Data Scientist - Lead Programmer Analyst

BILVANTIS TECHNOLOGIES

Technical Skills & Qualifications
• Experience should have 5 to 8 years of relevant work experience including 5 years of experience in developing algorithms using data science technologies to evaluate data scenarios and future outcomes.
• Advanced degree in data science, statistics, computer science, or equivalent with a background in statistics.
• Experience with healthcare and/or insurance data is a plus.
• Proficiency in SQL, Python/R, NLP and LLM
• Competent in machine learning principles and techniques.
• Experience with cloud-based data and AI solutions.
• Familiarity with collaboration environments (e.g. Jupyter notebooks,gitlab ,github etc)

Roles & Responsibilities
• Understand the business objective and directly participate in the delivery of data science solutions to solve business problems.
• Extract and analyze data: assessing quality, profiling, cleansing, exploratory data analysis and the transformation of large and complex datasets to be utilized for developing statistical models.
• Design and develop different statistical modeling techniques such as regression, classification models, anomaly detection models, clustering models, deep learning models and feature reduction etc. to derive actionable insights.
• Build, test, validate models through various relevant methodologies, error metrics and calibration techniques.
• Validate the post-production model performance and calculate the ROI.
• Work with cloud analytic platforms on AWS/ Azure using PySpark, Sage maker, Snowflake, Azure Synapse Analytics, etc.
• Perform multiple tasks and deal with changing deadline requirements. This includes knowing when to escalate issues. Maintain a focused, flexible, organized, proactive and positive behavior and approach.
• Monitor the projects of Junior Data Scientists, and mentor and provide them guidance when needed.
• Proactively provide recommendations to the business based on the insights derived from data science modeling techniques to resolve business problems.
• Communicate data science models’ complex results and the insights to the non-technical audiences.
• Interact with cross-functional technical teams and multiple business stakeholders to support integration of data science solutions into the business processes.