Senior Data Scientist – Metals Division

Job Title: Senior Data Scientist – Metals Division Company: TVARIT Job Category: Data Science Roles & Responsibilities: About TVARIT TVARIT GmbH specializes in developing and delivering cutting\-edge artificial intelligence (AI) solutions for the metal industry, including steel, aluminum, copper, cast iron, and more. Our software products empower customers to make intelligent, data\-driven decisions, driving About The Role We are looking for a Senior Data Scientist to join TVARIT’s Metals Division and build AI solutions that improve quality, yield, equipment reliability, and energy efficiency in metal manufacturing plants. The role suits a hands\-on data scientist who has worked with real plant data, is fluent in time\-series analysis and its tooling, and brings expertise in at least one metal manufacturing domain. Key Responsibilities Data Science \& Modeling * Develop, validate, and improve ML and statistical models for Predictive Quality, Predictive Maintenance, and energy optimization use cases in metal production and processing. * Analyze high\-frequency and batch time\-series data from sensors, PLC/SCADA systems, historians, MES, and quality labs, including alignment of process signals with quality and batch or coil/heat records. * Engineer features that capture process behavior, such as lags, rolling statistics, frequency\-domain features, process\-stage segmentation, and physically meaningful derived variables. * Apply anomaly detection, forecasting, classification, regression, and root\-cause analysis techniques, and combine data\-driven models with process knowledge and physics\-based constraints where appropriate. * Design robust validation strategies for time\-dependent data (e.g., time\-based splits, leakage checks, drift analysis) and quantify model uncertainty and limitations clearly. Delivery \& Productization * Contribute to end\-to\-end project delivery, from data onboarding and exploratory analysis through model deployment, monitoring, and retraining, working closely with software and data engineering teams. * Write clean, tested, reusable Python code and contribute to shared libraries, pipelines, and model templates that can be configured across plants and customers. * Mentor junior data scientists, review their analyses and modeling approaches, and help establish best practices for industrial data science within the division. Work Location: Pune Division, Maharashtra, India Availability: Full time

Qualifications * Bachelor’s or master’s degree in Data Science, Computer Science, Statistics, Mathematics, Metallurgical, Mechanical, Electrical, or Chemical Engineering, or a related discipline; a PhD is an advantage. * 7\+ years of overall professional experience, with substantial hands\-on experience in data science or machine learning applied to industrial or manufacturing data. * Mandatory manufacturing plant experience: has worked on data science projects with manufacturing plants, using real process, sensor, and quality data, and working directly with plant operations or engineering teams. * Mandatory time\-series expertise: strong practical experience in time\-series data analysis, including preprocessing, resampling, alignment, feature engineering, anomaly detection, forecasting, and validation of time\-dependent models. * Metal manufacturing domain expertise: in\-depth knowledge of at least one metal manufacturing domain, such as steelmaking (BOF/EAF, secondary metallurgy, continuous casting), aluminum (casting, rolling, extrusion), copper, foundry/die casting, forging, rolling, or heat treatment, including the key process variables and quality drivers. * Proven track record of taking models beyond notebooks to validated, deployed solutions that delivered measurable business impact. * Strong written and verbal communication skills, with the ability to explain technical results to both engineers and non\-technical stakeholders. * Willingness to travel to customer plants as required. Technical Skills * Programming: Strong Python skills for data science, with working proficiency in SQL; good coding practices including version control (Git), modular code, and testing. * Data analysis libraries: pandas, NumPy, SciPy, and visualization libraries such as Matplotlib, Plotly, or Seaborn. * Time\-series and ML packages: Hands\-on experience with packages such as statsmodels, scikit\-learn, sktime, tsfresh, Darts, or Prophet, and gradient boosting libraries such as XGBoost or LightGBM. * Deep learning: Experience with PyTorch or TensorFlow for sequence models (e.g., LSTM, temporal CNN, transformer\-based models) is an advantage. * Statistics and modeling: Solid foundation in statistics, signal processing, regression, classification, anomaly detection, forecasting, and design of Key Skills: Data Science Salary: Not disclosed