Job Title: Senior Data Scientist OVA5886 Company: Ovations Technologies Job Category: Machine Learning Roles & Responsibilities: Build, deploy, scale and support machine learning, AI and GenAI solutions in production. The role focuses on operationalising models, developing AI applications and agents, and creating the platforms and services required to deliver business value at scale. Key Responsibilities * Productionise, deploy and monitor machine learning models and data science pipelines on Databricks. * Build, deploy and support AI Agents, GenAI applications and RAG solutions on Databricks. * Develop and maintain reusable ML pipelines using MLOps principles, including CI/CD, automated testing, monitoring and governance. * Deploy, optimise and manage open source AI and machine learning models on Azure Kubernetes Service (AKS). * Design, develop and support custom APIs and microservices on AKS to expose AI and machine learning capabilities to business applications. * Implement containerised solutions using Docker and Kubernetes to ensure scalable, secure and resilient deployments. * Monitor model performance, drift, reliability and operational health in production environments. * Partner with Data Scientists to productionise prototypes and enable business\-ready solutions. * Collaborate with platform, security, cloud and infrastructure teams to ensure compliance with enterprise standards. * Troubleshoot and resolve production issues related to models, pipelines, APIs and AI applications. * Optimise AI and ML solutions for performance, scalability, cost and reliability. * Contribute to engineering standards, reusable frameworks and best practices across the AI and ML ecosystem. * Mentor junior engineers and promote knowledge sharing across the team. * Stay current with advancements in AI, GenAI, MLOps, Databricks, Kubernetes and cloud technologies. Core Deliverables * Production\-ready ML models and pipelines running on Databricks. * AI Agents and business applications deployed on Databricks. * Open source LLMs and AI services deployed on AKS. * Secure and scalable APIs exposing AI capabilities to consuming systems. * Automated deployment, monitoring and governance processes. * Reliable, scalable and compliant AI platforms supporting business outcomes. * Databricks Workflows, Model Serving, MLflow and Mosaic AI * Azure Kubernetes Service (AKS) * Python, SQL and REST APIs * Docker and Kubernetes * CI/CD and MLOps practices * Machine Learning and Generative AI * LLM deployment and optimisation * Cloud engineering and infrastructure automation * Monitoring, observability and troubleshooting * Strong understanding of MLOps, DevOps and software engineering practices for machine learning platforms. * Experience building, deploying and supporting machine learning solutions in production environments. * Proficiency in Python and experience with SQL and API development. * Experience with Databricks, MLflow, Model Serving and cloud\-native AI/ML platforms. * Hands\-on experience with Kubernetes, Docker and containerised application deployment. * Experience deploying and supporting machine learning and Generative AI solutions on Azure Kubernetes Service (AKS). * Knowledge of CI/CD pipelines, infrastructure automation and platform monitoring. * Experience with distributed computing technologies such as Spark and large\-scale data processing frameworks. * Understanding of machine learning, large language models (LLMs), retrieval\-augmented generation (RAG) and AI agents. * Ability to productionise data science solutions and collaborate effectively with Data Scientists. * Experience delivering end\-to\-end AI and machine learning use cases from development to production. * Ability to translate technical concepts into business outcomes and communicate effectively with stakeholders. * Strong written and verbal communication skills with the ability to work across cross\-functional teams. * Self\-driven, adaptable and capable of thriving in a fast\-paced, technology\-driven environment. Work Location: Johannesburg, GP, South Africa Availability: Full time
Job Title: Senior Data Scientist Company: Incubeta Job Category: Machine Learning Roles & Responsibilities: As a Senior Data Scientist within our Marketing Intelligence team, you will play a key role in the development and implementation of advanced data services and solutions, leveraging machine learning (ML) and artificial intelligence (AI) to drive profitability and value for our clients. You will possess a deep understanding of data science principles, advanced analytics techniques, and machine learning engineering and be proficient in utilising a range of programming languages, tools and machine learning frameworks You will be required to collaborate effectively with cross\-functional teams \- including internal \& external stakeholders \- to understand clients' data needs and translate them into effective technical solutions. They will lead on advanced data projects, and develop data science approaches that can be adopted across the team. Your achievements will lead to recognition as a thought leader, actively contributing to the company's reputation and brand through your expertise and insights. Key Responsibilities Your tasks and responsibilities will include but are not limited to: * Apply advanced statistical modeling and techniques to solve complex business challenges. * Develop and implement advanced data solutions using machine learning (ML) and artificial intelligence (AI) to drive profitability and value for clients. * Design and build machine learning pipelines and models, optimising their performance and efficiency * Develop and maintain machine learning infrastructure. * Track and document model lineage, model versioning and model transitioning. * Independently design experiments, build algorithms, and apply machine learning tools to manage, interrogate, and extract value from large data sets for the benefit of media \& digital marketing strategy * Lead advanced data projects and develop data science approaches that can be adopted across the team. * Demonstrate a willingness to learn and teach. Provide technical guidance and mentorship to junior data scientists, fostering their growth and ensuring the quality of their work. * Collaborate effectively with cross\-functional teams, including internal \& external stakeholders, to understand clients' data needs and translate them into effective technical solutions. * Proactively share information on the latest updates, new products, and evolving processes with the team. * Demonstrate data knowledge and expertise through training materials and address clients' data challenges in senior\-level discussions. * Lead training sessions \& workshops with clients on technical data topics within your expertise. * Cultivate a strong relationship with clients and suppliers, which results in Incubeta being seen as a trusted partner and technical expert in the solutions offered. * Deliver code and solutions that are well documented and understandable by other data scientists. * Take responsibility for security in PII data, code management, and deployment. * Proactively identify improvement opportunities for existing and new solutions across the business based on GP opportunity, customer feedback and industry needs. * Improve on modeling techniques in our existing solutions and suggest new products through learning and development KPI \& Performance Management The Senior Data Scientist reports directly to the EMEA Director of Data Science. This role is based in our Cape Town office and operates within the EMEA Marketing Intelligence team. You will be an integral part of a dynamic and collaborative team, working alongside marketing intelligence professionals across EMEA, including: * Data Analysts * Data Engineers * Analytics Implementation Specialists * Data \& Analytics Consultants * Technical Project Managers * 8\+ years of experience in data science, with a focus on machine learning, artificial intelligence, and advanced analytics. * Advanced expertise in data analysis, statistical modeling, and developing machine learning models for analysis and predictions * Experience in designing and building machine learning pipelines and deploying ML models to production environments. * An expert in programming languages SQL, Python, R * Advanced experience with cloud platforms GCP (Essential), AWS, Azure (Advantageous) and cloud based ML services (e.g. Vertex AI). * Demonstrable competency in propensity modelling, churn analysis, causal impact analysis, pLTV and MMM * Familiarity with ML frameworks like TensorFlow, PyTorch, scikit\-learn, and others * Experience in pipeline orchestration tools such as Dataform, DBT and Apache Airflow * Experience in developing and implementing advanced measurement solutions for digital marketing activation including MMM, Incrementality Testing, Causal impact Analysis \& Attribution modelling * Experience in customer and audience modelling solutions including propensity modelling, predictive lifetime value, churn analysis or similar * Knowledge of industry trends and the ability to apply this knowledge to technical client solutions. * Understanding of Analytics platforms \& impact of data within \- in particular Google Analytics 4 * Knowledge of Key Metrics and Dimensions for GMP Platforms (DV360, CM360, SA360\), Google Ads and Facebook * Knowledge of Digital Media Strategy * Can confidently deal with breadth of enquiries around analytics \& data * Skilled in utilising Google Workspace for communication, presentations and reporting (e.g. Gmail, Google Slides, Google Docs, Google Sheets) * Google Cloud Platform certified (Advantageous) * ETL tooling experience (FiveTran, Supermetrics) (Advantageous) * Excellent communication and presentation skills * Demonstrable expertise in strong client relationship management, and experience of building and managing key partner relationships * Strong technical storyteller \- able to turn complex technical concepts into simple and easy to understand concepts for both internal teams and clients * Excellent problem\-solving abilities * Ability to work in a fast paced, dynamic, pro\-learning environment on multiple priorities simultaneously * Relentless enthusiasm and highly reliable * Accountable, driven, and proactive in development and making progress * Display a hunger for knowledge and a willingness to learn for any situation * Ability to think and act independently as well as collaboratively * Can establish influential relationships with both internal and external stakeholders * Exceptional attention to detail and pride in any work produced * Strong ability to prioritise and manage own time as well as assisting teams where needed * Display honesty and * Earn Trust: Focused on maintaining and increasing customer focus, works with the customer in mind earning trust along each step with clients and colleagues * Help Others Succeed: Makes an active contribution to a joint result (organizational target) or to resolving problems or conflicts. Think in terms of possibilities. Is able to solve issues and problems in a timely and effective manner, working as a team. * Dare to Lead: Is willing to improve individual performance and stimulate others to act accordingly. Setting the standard and example. Being solution driven. * Exceed Expectations: Carries out tasks without help from others; takes command of problem situations and aims to achieve more than the basic requirements Work Location: City of Cape Town, Western Cape, South Africa Availability: Full time
Job Title: Senior Machine Learning Engineer - AI / GenAI at Datonomy Solutions Company: Datonomy Solutions Job Category: Machine Learning Roles & Responsibilities: Role Overview The role is strongly focused on the productionisation and operationalisation of AI and ML solutions, including machine learning models, GenAI applications, AI agents, Retrieval\-Augmented Generation (RAG) solutions and reusable ML platforms. This is an engineering\-focused role requiring strong experience taking AI and Machine Learning solutions from prototype through deployment, scaling, monitoring and production support. Key Responsibilities * Design, build, deploy and support production\-grade Machine Learning and AI solutions. * Productionise Machine Learning models and Data Science pipelines using Databricks. * Develop and deploy Generative AI applications, AI agents and RAG solutions. * Build reusable Machine Learning pipelines and frameworks using MLOps principles. * Implement CI/CD, automated testing, model monitoring, governance and deployment automation. * Deploy and optimise open\-source Machine Learning and Large Language Models within Azure Kubernetes Service (AKS). * Develop and support REST APIs and microservices that expose AI and Machine Learning capabilities to enterprise applications. * Build scalable containerised solutions using Docker and Kubernetes. * Implement and maintain Databricks Workflows, MLflow, Model Serving and Mosaic AI solutions. * Monitor models for performance degradation, model drift, reliability and operational health. * Troubleshoot production issues across models, ML pipelines, APIs, GenAI applications and supporting infrastructure. * Optimise AI and ML platforms for performance, scalability, reliability and cost efficiency. * Collaborate with Data Scientists to convert models and prototypes into business\-ready production solutions. * Partner with Cloud, Infrastructure, Security and Platform Engineering teams to ensure solutions comply with enterprise architecture and security standards. * Contribute to reusable engineering frameworks, standards and best practices across the AI and Machine Learning ecosystem. * Mentor junior engineers and support knowledge sharing within the engineering team. Candidates Should Have Strong Hands\-on Experience In * Python * SQL * Databricks * Databricks Workflows * MLflow * Databricks Model Serving * Mosaic AI * Microsoft Azure * Azure Kubernetes Service (AKS) * Kubernetes * Docker * REST API development * Microservices * CI/CD * MLOps * Machine Learning * Generative AI * Large Language Models (LLMs) * Retrieval\-Augmented Generation (RAG) * AI Agents * Spark / distributed computing * Cloud\-native AI/ML platforms * Monitoring and observability * Infrastructure automation * Strong experience building, deploying and supporting Machine Learning solutions in production environments. * Proven experience with MLOps, DevOps and software engineering practices within AI/ML environments. * Strong development experience using Python, together with SQL and API development. * Hands\-on experience with Databricks, MLflow and Model Serving. * Strong experience with Kubernetes, Docker and containerised application deployment. * Experience deploying Machine Learning and/or Generative AI workloads onto Azure Kubernetes Service (AKS). * Experience implementing CI/CD pipelines, infrastructure automation and production monitoring. * Experience with Spark or other distributed / large\-scale data processing technologies. * Practical understanding of LLMs, RAG architectures, Generative AI and AI agents. * Experience taking AI or Machine Learning use cases from development through to production deployment and support. * Experience working collaboratively with Data Scientists and engineering teams. * Strong troubleshooting skills across applications, APIs, ML pipelines and cloud platforms. * Ability to translate technical solutions into measurable business outcomes. The strongest candidates will have previously built and supported enterprise AI/ML platforms, rather than only developing models in notebook or research environments. They should be comfortable working across the full lifecycle from model development and experimentation through to APIs, containers, Kubernetes deployments, monitoring, governance and ongoing production support. Work Location: Emfuleni, Gauteng, South Africa Availability: Full time
Job Title: Group Principal Engineer Company: NTT DATA Job Category: Machine Learning Roles & Responsibilities: Summary of Role We are seeking a Group Principal Engineer for Control \& Instrumentation (C\&I) to spearhead the adoption of operational technology (OT) and Artificial Intelligence (AI) within the group. The role will ensure governed convergence of traditional industrial automation, information technology (IT) and artificial intelligence architectures to improve operational processes in a secure manner while minimising cybersecurity vulnerabilities. Responsibilities * Strategic Leadership: Define the short, medium and long\-term technical roadmaps for C\&I and AI across operations, moving from isolated site\-based interventions to integrated, federated systems. * AI \& Automation: Provide technical specialist consultancy on the development and implementation of complex advanced control system architectures (PLC, DCS, SCADA) and ensure seamless integration with Digital Twins for real\-time simulation and decision support. * Digital Transformation: Drive the shift toward low\-code integrated digital platforms to empower developers across the organisation to bring to bear innovative solutions from already deployed operational technology systems. * OT and AI Governance: Provide effective oversight and assurance on the design, implementation and maintenance of cybersecurity security controls. * Develop and maintain policies, procedures, and standards for the OT environment and AI landscape * Deployed architectures to comply with evolving international standards and best practices for OT and AI systems to minimise operational and business risk. * Security risk assessments to be performed on Industrial Control Networks, OT infrastructure, AI systems, and applications to identify vulnerabilities and weaknesses * Respond to security incidents, assisting in the containment, eradication, and recovery phases. Knowledge of disaster recovery, computer forensic tools, technologies and methods * OT and AI Projects: Conduct technical reviews on proposed AI and OT capital projects to ensure the business cases and associated technically solution are sound and rational, and meet the governance requirements. * Deliver quarterly and annual reports on AI and OT governance. * Convenor of OT and IT working group steering committees. Work Location: Johannesburg, GP, South Africa Availability: Full time
Job Title: Intermediate Data Solutions Engineer Company: Entelect Job Category: Machine Learning Roles & Responsibilities: Join a dynamic, multidisciplinary environment that encourages versatility, and provides individual attention to career development, including intentional and regular one\-on\-ones with leadership. Tackle complex challenges and make a meaningful impact alongside passionate professionals who inspire and support each other. * We like to play to your individual strengths, but having a solid understanding of the entire data analytics value chain, from source data and medallion architectures, through to reporting, dashboarding, advanced analytics, and AI solutions, is advantageous. * Wrangling data from multiple sources into a reliable, evolving data asset. * Collaborating with Solution Architects to design and implement cloud, hybrid, and on\-prem solutions. * Designing and implementing optimal data engineering pipelines and, if this is within your wheelhouse, delivering impactful advanced analytics using AI and ML/data science. * Working across descriptive, diagnostic, predictive, prescriptive, and cognitive analytics. * Understanding DataOps and MLOps to ensure continuous deployment in short cycles with maximum impact and high quality. * Maintaining data lineage and metadata effectively. * Using technical and data analytics expertise to improve business innovations. * Demonstrating maturity, time management and motivation in the workplace. * Championing change within the team and fostering a collaborative culture. * Collaborating with cross\-functional teams to tackle complex business challenges, driving continuous improvement. * Upholding standards of excellence and accountability. Work Location: City of Cape Town, Western Cape, South Africa Availability: Full time
Job Title: SAP AI Engineering Architect (f/m/d) Company: SAP Job Category: Machine Learning Roles & Responsibilities: We are seeking an experienced SAP AI Engineering Architect (f/m/d) to lead the design, development, and implementation of AI\-driven solutions within our SAP ecosystem. The ideal candidate will possess deep expertise in SAP technologies, AI frameworks, and SAP Business Technology Platform (BTP), ensuring seamless integration and optimization of AI capabilities to drive business innovation and efficiency. What You‘ll Build * Architect and implement AI solutions within SAP systems to enhance business processes and decision\-making. * Design and deploy scalable, secure, and high\-performing AI powered applications on SAP BTP. * Utilize SAP BTP services such as SAP Generative AI Hub, SAP AI Core, SAP AI Business Services, SAP Integration Suite etc. to develop innovative solutions. * Develop and manage APIs and microservices using SAP BTP tools for seamless integration across SAP and non\-SAP systems. * Leverage SAP BTP capabilities for data orchestration, analytics, and machine learning model deployment. * Collaborate with cross\-functional teams to identify opportunities for AI integration and automation within SAP landscapes. * Optimize SAP systems for AI\-driven analytics, predictive insights, and real\-time decision\-making. * Ensure compliance with data governance, security standards, and SAP best practices. * Provide technical leadership and mentorship to development teams. * Stay updated on emerging AI technologies and SAP BTP advancements to drive innovation. Bring out your best SAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a market leader in end\-to\-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose\-driven and future\-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best. For SAP employees: Only permanent roles are eligible for the SAP Employee Referral Program, according to the eligibility rules set in the SAP Referral Policy. Specific conditions may apply for roles in Vocational Training. Work Location: Pretoria, Gauteng, South Africa Availability: Full time
Job Title: Advanced Analytics: Machine Learning Engineer Company: Momentum Corporate Job Category: Machine Learning Roles & Responsibilities: Role Purpose Drive the productionisation, operationalisation, and lifecycle management of advanced analytics and machine learning models within Momentum Corporate, with the appropriate incorporation of AI\-based models. The role transforms actuarial and data science prototypes into robust, scalable, auditable production systems in a regulated insurance context \- partnering closely with Actuarial, Data Science, and IT to ensure reliability, explainability, and value at scale. Duties \& Responsibilities INTERNAL PROCESS * Actively participates in the design, build and deployment of the solution to a set problem statement by applying advanced analytics, with a particular focus on: * Driving the transformation of actuarial and data science prototypes into production\-grade services (batch jobs and/or APIs), including packaging, testing, documentation, and release. * For mature projects and data pipelines establish Continuous Integration or Continuous Delivery (CI/CD) for ML including scalable, automated scoring pipelines and selecting fit\-for\-purpose architectures (scheduled batch vs. real\-time API). * For new/emerging projects and data pipelines establish tactical data pipelines where appropriate. * Design, deploy, and operate ML pipelines that can leverage Microsoft Fabric artefacts developed by actuarial and other business facing specialists (Lakehouse, Notebooks, Data Pipelines, Warehouse, OneLake). * Partner with Data Engineering and IT to ensure reliable, well\-documented data inputs for training, scoring, and monitoring. * Collaborate with specialists in IT to align on consistent standards and hybrid solutions. * Support the use of machine learning techniques or tools to select features, create and optimise models. Where relevant, aid in the preprocessing of structured and unstructured data, including AI\-based techniques. * Prepare pipelines and deployment patterns that can integrate or leverage off of AI/LLM components (retrieval, orchestration, evaluation) where relevant. * Standardise repository structures, coding conventions, and templates for reusable model components and jobs. * Strategic implementation of ML and AI\-based solutions to enhance model efficiency. * Promote reusability, maintainability, reliability, and scalability in design and development of model solutions. * Embed governance by design: model cards, validation evidence, performance baselines, approvals and sign\-offs for regulated use. * Ensure explainability and auditability of production solutions (feature attributions/diagnostics appropriate to the model class and use case). * Act as the technical interface to IT for environments, network/security patterns, deployment pathways, and operational readiness. * Collaborate closely with actuaries and data scientists to production\-align model design (latency, throughput, cost, explainability, stability). * Provide technical enablement to the analytics team (templates, how\-to guides, code reviews) to uplift production\-ready practices. * Design for cost efficiency across compute, storage, and orchestration; monitor runtime costs and optimise resource utilisation. * Support model performance by catering for monitoring data drift and re\-training systems. * Recommend and implement MLOps tooling (experiment tracking, model registry, orchestration, monitoring) and evolve standards across the team. * Keep current with platform capabilities (e.g. Azure ML vs AWS vs Databricks, containerisation, orchestration services) and introduce pragmatic improvements that reduce risk and lead time. * Collaborating with cross\-functional teams: build and maintain relationships with clients and stakeholders that promote cross\-delivery practice solutions. * Maintain strong relationships with Actuarial, Data Science, IT, and business owners to deliver reliable production ML services that meet agreed SLAs/SLOs. * Represent Advanced Analytics in architecture, security, and governance forums to ensure compliant deployment patterns. * Continuously improve turnaround times, stability, and observability of ML services; proactively communicate incidents and remediation. * Champion solution explainability and auditability to support business adoption and regulatory expectations. * Establish and socialise engineering standards for model packaging, testing, observability, and documentation. * Operate effectively in a lean, business\-embedded advanced analytics team, balancing tactical delivery with the progressive maturation of engineering practices. * Coach actuaries and data scientists on production\-ready coding practices (data contracts, dependency management, testing, reproducibility). * Contribute to a culture of continuous improvement and incident learning (post\-mortems, blameless RCA, playbooks). * Participate in a positive work climate and culture to energise employees and give meaning to work. * Promote a culture that guides and directs best practice, fostering an environment of continuous learning, improvement and cohesiveness. * Participate in a learning and growth culture whereby information regarding successes, issues, trends and ideas are actively shared within Momentum Corporate but also at Group level, through participation in Group\-wide Forums * Participate in performance management frameworks in line with Momentum Corporate’s guidelines. * Encourage innovation, change agility and collaboration within the team. * Design for cost efficiency across compute, storage, and orchestration; monitor and optimise runtime costs of pipelines and model endpoints. * Implement controls that reduce operational and compliance risk (access, secrets, data minimisation, retention). * Track and report service reliability metrics (uptime, latency, failure rates) and cost KPIs relevant to production ML operations. * Optimally contribute to the budget design for the area, including the motivation for expenditures and implementation of financial regulations. * Follow risk management, governance, and compliance policies in own practice area, to identify and manage risk exposure liability. Competencies * End\-to\-End Ownership \& Delivery: Takes accountability for production ML services from deployment through operation, monitoring, and continuous improvement. * Systems Thinking \& Engineering Rigor: Designs solutions with reliability, observability, scalability, and full lifecycle management in mind. * Pragmatic Problem\-solving: Favors simple, robust, and maintainable solutions that balance technical excellence, risk, maturity of business area and business value. * Collaboration \& Technical Influence: Effectively bridges Actuarial, Data Science, IT, and business stakeholders through clear, credible technical communication. * Governance \& Compliance Mindset: Embeds auditability, explainability, security, and regulatory compliance by design in all production solutions. * Continuous Improvement \& Innovation: Drives automation, learning from incidents (post\-mortems), and incremental innovation to improve reliability and speed to value. * Business \& Stakeholder Acumen: Demonstrates understanding of the group insurance environment and a strong commitment to stakeholder outcomes and service excellence. * Professional Impact \& Team Contribution: Shows self\-awareness, inclusiveness, and a collaborative mindset; contributes to growing team capability and supporting change. Work Location: Cape Town, WC, South Africa Availability: Full time
Job Title: Senior Machine Learning Engineer Company: Mr D Job Category: Machine Learning Roles & Responsibilities: * Scale : Millions of happy shoppers use Mr D. Show them what you can do. * Learn : We work with the best of the best, and then some. Work alongside industry leaders and up\-skill in record time. * Grow : Expand your career in the fast\-growing Takealot Group: takealot.com, Mr D \& Takealot Fulfilment Solutions (TFS). We like to promote from within: Here’s your chance. This position reports to the Machine Learning Engineering Manager. As a Machine Learning Engineer, your expertise directly translates into the development of robust machine learning models in the e\-commerce domain . You come from a development background with an emphasis on applied machine learning . You will have the opportunity to implement best practices to improve the existing machine learning models and participate in the development of new ones. This is an exciting opportunity to contribute to a growing and dynamic team of machine learning engineers working on Mr D’s ambitious development roadmap. Your responsibilities will include: * Work with a firehose of customer data. * Analyse, build, evaluate, and productionise machine learning models. * Touch all aspects of the business in your domain. * Brainstorm new ideas and concepts to solve specific use cases. * Work with teammates on design and code reviews. * Keep abreast of developments in the machine learning domain. * No doors : We aren’t fans of stuffy offices or siloed work environments. See someone you like? High five, collab, and make something great. * Remote working : Love water cooler chats and working from home? Takealot.com offers a remote or hybrid working model for the best of both worlds. * Mentorship programme : We aren’t kidding when we say the people with the best people win. Now’s your chance to be one of the best by learning from the best. * Naspers Tech Community \& Online Learning : Share ideas and grow with global industry leaders who are all just a Slack message away. Love to learn? Upskill with free access to courses on MyAcademy, Udacity, Coursera, and more. * Good times : Get to know the other extraordinary minds at takealot.com during regular social events and out\-of\-office activities (think hikes, think mini golf, think good times). * Staff discount . Millions of products across 28 departments. What more could you ask for? * Birthday leave : Cake Day all the way. Enjoy your day off \- you deserve it. * Right tools, right job : You’ll work on the latest tech * Help when you need it most : Confidential counselling, legal support, and financial guidance, for free, anytime, anywhere. * Tech stack (for days): Python, AWS, Kafka, Redis, Docker, Kubernetes, IOT/MQTT and all things machine learning. If you build it, they will come. * Free parking: No more 5km fun runs to your desk (unless you want to). Work Location: South Africa Availability: Full time
Job Title: Head of Data Analytics Company: GoTyme ZA (South Africa) Job Category: Machine Learning Roles & Responsibilities: Role Purpose ---------------- The Head of Data Analytics leads GoTyme Bank's business\-facing data science capability across Marketing, Fraud, AML, Customer Experience, Distribution and Partnerships. You will turn company and functional priorities into a clear analytics roadmap, lead and develop a team of data scientists and senior data scientists, and ensure that the team's work changes decisions and improves measurable business outcomes. This is a senior people and delivery leadership role with strong technical credibility. You will move the function from reactive request fulfilment towards proactive insight, experimentation and responsible use of advanced analytics and AI. You will work closely with Reporting and Insights, Data Governance, Data Engineering, Finance, Risk, Product and Lending Analytics so that decision makers receive trusted, timely and well\-governed analysis. Key Responsibilities ------------------------ * Analytics Strategy and Business Impact * + Set and own the data analytics strategy and roadmap for the functions in scope, aligned to GoTyme Bank's company priorities and measurable business outcomes. + Direct proactive analysis across customer acquisition, activation, engagement, segmentation, revenue, fraud prevention, AML effectiveness, customer experience, distribution performance and partner value. + Frame the right business questions, identify opportunities before they become requests, and ensure insights are translated into decisions, actions and measurable impact. + Establish clear success measures for major analytics initiatives and track whether recommendations are adopted and deliver the intended result. * People Leadership and Capability * + Act as the primary line manager for data scientists and senior data scientists within the function, owning performance, development and day\-to\-day effectiveness. + Recruit, onboard, coach and retain strong analytical talent; set clear expectations and provide regular, honest and developmental feedback. + Build succession and capability plans, create opportunities for team members to take on greater responsibility, and address performance or capability gaps early. + Create a team environment where people collaborate across domains, share knowledge, raise risks openly and maintain high standards without losing pace. * Prioritisation Planning and Delivery * + Run a transparent intake and prioritisation process so that analytical capacity is directed to the highest\-value work across the functions in scope. + Maintain a visible roadmap, stakeholder service levels and a regular operating cadence covering delivery, impact, capacity, risks and dependencies. + Own the quality and timeliness of the team's output; resolve blockers, coordinate cross\-team dependencies and escalate trade\-offs early when capacity or priorities conflict. + Clarify ownership and hand\-offs with Reporting and Insights, Data Governance, Data Engineering and Lending Analytics to avoid duplicated work or gaps in delivery. * Stakeholder Leadership * + Act as the senior analytics partner for Marketing, Fraud, AML, Customer Experience, Distribution and Partnerships, building trusted relationships with functional leaders and their teams. + Communicate analytical strategy, evidence, trade\-offs and recommendations clearly to executive, commercial, risk and technical audiences. + Represent Data Analytics in relevant governance, planning and performance forums, ensuring that evidence is used in material decisions. + Challenge assumptions constructively, establish common definitions and help stakeholders distinguish useful analysis from activity that will not change a decision. * Analytical Standards Experimentation and Innovation * + Set consistent standards for analytical design, SQL and Python development, peer review, documentation, reproducibility, quality assurance and communication of results. + Build disciplined test\-and\-learn practices for campaigns, customer journeys, fraud controls, segmentation, channel interventions and partner initiatives. + Apply advanced analytics, machine learning and generative AI where they can improve decisions, turnaround time or efficiency, with clear measures of value and appropriate human oversight. + Partner with platform, engineering and reporting teams to create reusable data products, scalable analytical assets and well\-governed self\-service capabilities. * Governance Risk and Compliance * + Ensure that the function complies with data governance, privacy, information security, model governance and regulatory requirements, including appropriate access controls and audit trails. + Maintain documented review and monitoring processes for analytical outputs used in customer, risk, executive or regulatory decisions. + Identify and escalate material data quality, bias, control or interpretation risks, and ensure agreed remediation is completed. + Support audit and regulatory requests relevant to the function and ensure that all team members complete required compliance training. * 0-2 years of experience in data science, business analytics or a related quantitative field, including substantial people leadership responsibility. * Demonstrated success leading senior analytical talent and delivering high\-quality work through teams rather than relying on personal contribution alone. * Track record of using analytics to improve commercial, customer, operational or risk outcomes in a complex organisation. * Experience managing a portfolio of competing work and influencing senior stakeholders when priorities, capacity or evidence are contested. * Strong technical grounding in statistics, experimentation, predictive modelling and data science, with current proficiency in SQL and Python sufficient to review and guide the team's work. * Commercial judgement and the ability to connect analytical work to customer, revenue, cost, risk, control and service outcomes. * Strong people leadership across performance management, coaching, feedback, hiring and career development. * Clear, structured communication across technical and non\-technical audiences, including executive\-level recommendations and difficult stakeholder conversations. * Strong prioritisation, portfolio planning and delivery management, with the ability to make transparent decisions under ambiguity and capacity constraints. * Sound understanding of data quality, privacy, analytical governance and the controls required in a regulated environment. * Experience in retail banking, fintech or another regulated consumer business. * Domain experience in more than one of marketing analytics, customer analytics, fraud, financial crime, distribution analytics or partner data. * Experience with modern cloud analytics platforms such as Databricks and with team\-based development practices including Git and code review. * Experience with analytical governance, model risk, regulatory reporting, audit remediation or privacy requirements such as POPIA. * Experience leading teams through organisational change, rapid growth or a shift from reactive requests to proactive analytics. * People\-first leader who invests in the growth, performance and wellbeing of the team. * Proactive and commercially curious; looks for important questions and opportunities rather than waiting for a request. * Pragmatic and evidence\-led; balances analytical rigour with pace, capacity and business need. * Direct and fair; gives clear feedback, handles difficult conversations and makes trade\-offs visible. * Calm and dependable under pressure, providing structure and direction when priorities are ambiguous or changing. * Collaborative across functions and willing to challenge constructively when the evidence requires it. * EAP support (counselling, financial and legal for you and anyone financially dependent on you). * Competitive, industry\-benchmarked salaries. * Non\-guaranteed performance bonuses. * Access to company shares (T's \& C's apply). * Wellness days. * Social clubs. * On\-site barista. Work Location: Cape Town, WC, South Africa Availability: Full time
Job Title: Quantitative Analyst Company: Nedbank Job Category: Machine Learning Roles & Responsibilities: Leadership Pipeline ----------------------- Manage Self: Professional FAIS Affected ----------------- Support the development, refinement, testing, deployment, and maintenance of quantitative models, dashboards, and analytics solutions in accordance with group methodologies and/or industry best practices. Build foundational expertise in data science, risk modelling, and commercial acumen to enable independent delivery of high\-impact solutions across the Nedbank value chain. They support senior quantitative analytics teams by collecting, cleaning, and analysing data that forms the foundation for complex quantitative solutions. Job Responsibilities ------------------------ * Acquire and continuously develop/improve deep expertise in banking products, services and risk management. * Acquire and continuously develop/improve deep domain expertise in quantitative analytics and their application across various risk types and specialised functional. * Conduct research and analysis to build, maintain and continuously refine mathematical and statistical models and quantitative solutions. Collaboration and Development Model Design and Development Delivery management * Work and collaborate within and across teams, senior analysts and digital/technology teams to develop, maintain, enhance and deploy scalable models. * Support senior analysts and broader quantitative teams in the business change management and capability development processes related to the deployment of model/quantitative solutions. * Ensure agile delivery, cross functional collaboration and deployment readiness of models. Job Responsibilities Continue --------------------------------- * Participate in multi\-disciplinary/cross functional/business teams to design and deploy quant solutions. * Provide and receive peer reviews and constructive feedback. * Adopt a development mindset, demonstrating commitment, learning agility and an independent drive for continuous improvement and growth towards independent design and delivery of quantitative solutions. * Participate as a member of agile teams (sometimes multiple simultaneously) and deliver within continuously evolving strategic contexts and domain areas. * Actively contribute to a culture of innovation and continuous learning within the greater quant community. * Comply with both internal risk governance frameworks and external regulations, ensuring both integrity of quantitative solutions developed and adherence to conduct requirements (e.g., independence between model development and validation, SAS MRM Standards.) * Develop an understanding of the regulatory requirements and various standards (e.g. IFRS, Basel, etc.), ensuring their effective implementation in all relevant quantitative solutions and model development processes. * Participate in and provide technical support for the remediation of regulatory, validation, peer review, and audit findings. * Support model risk mitigation through robust validation, monitoring and remediation practices Essential Qualifications \- NQF Level ----------------------------------------- * Demonstrates foundational understanding of quantitative methods and apply them to defined tasks; more complex analyses are completed with guidance and supervision. * Strong knowledge of probability and statistics * Technical understanding or exposure to statistical analysis and models * Strong analytical and problem\-solving skills and ability to translate complex data and model / solution outcomes into business insights. * Proficiency or familiarity in programming languages (e.g. Python, R, C\+\+, Java, SQL, SAS, VBA, MATLAB, etc.), with the ability to translate algorithms and mathematical or statistical models into flexible and scalable coding logic * Technical understanding or exposure to big data concepts, data structures, data storage, data extraction/mining. * Technical understanding or exposure of model lifecycle management i.e. development, validation, implementation, monitoring. * Technical understanding and exposure to AI/ML techniques and model explainability tools (e.g., SHAP, LIME etc.). * Some exposure to banking regulation and accounting standards preferred but not essential Behavioural Competencies ---------------------------- Work Location: Johannesburg, GP, South Africa Availability: Full time
