Job Description

Senior Data Scientist – Insurance Analytics

We are seeking a Senior Data Scientist (8–10 years of experience) to support an analytics initiative focused on the insurance domain. This is a client facing role requiring strong analytical expertise, hands-on modeling experience and the ability to independently drive analysis, present insights and collaborate with stakeholders.

The ideal candidate will have a solid foundation in statistical modeling and hypothesis testing, deep experience in tree-based and ensemble machine learning models, exposure to cloud-based data platforms and working knowledge of modern Generative AI and Large Language Model (LLM) techniques relevant to insurance analytics.

Key Responsibilities

• Develop and deploy fraud detection models for insurance claims using statistical, machine learning and Gen-AI augmented analytical techniques.
• Perform exploratory data analysis (EDA), feature engineering, and hypothesis testing to identify fraud patterns and anomalies.
• Build, evaluate, and optimize traditional statistical models as well as tree-based ML models such as Random Forest, XGBoost, CatBoost, and LightGBM.
• Explore and apply LLM based approaches (e.g. text classification, summarization, entity extraction) for leveraging unstructured data such as claim notes, adjuster comments and documents.
• Develop GenAI powered accelerators for documentation, feature ideation, data enrichment and model insight generation.
• Independently conduct data analysis, research, model experimentation and translate findings into actionable insights.
• Write clean, efficient and production ready code using Python and SQL.
• Work extensively with large datasets using cloud platforms, primarily Google Cloud Platform (GCP).
• Query and manage data using Big Query and datasets stored in Cloud Storage (Buckets).
• Use Git for version control, collaboration and code review.
• Prepare clear, concise and impactful presentations for clients, explaining analytical findings to both technical and nontechnical stakeholders.
• Collaborate with business, data engineering, and client teams to ensure models align with investigation strategies and broader business objectives

Required Skills & Experience

• 7–8 years of hands on experience in data science, analytics, or applied machine learning
• Strong understanding of statistical modeling, probability concepts, and hypothesis testing
• Proven experience with tree-based and ensemble machine learning models (RF, XGBoost, CatBoost, LightGBM)
• Experience working with unstructured data and NLP techniques, preferably including LLMs (OpenAI, Gemini, Llama, etc.)
• Practical exposure to GenAI workflows such as prompt engineering, fine tuning, retrieval augmented generation (RAG), or automated insight generation
• Expert‑level SQL for data extraction, transformation, and analysis
• Strong Python skills for data analysis, machine learning, and LLM based pipelines
• Experience using Git for source code management
• Solid exposure to cloud based analytics environments, preferably Google Cloud Platform (GCP), Big Query, and Cloud Storage
• Ability to work independently, manage deliverables and drive tasks end to end.
• Excellent verbal and written communication skills, essential for a client facing role.

Candidate Profile

• Bachelor’s/Master’s degree in economics, statistics, mathematics, computer science/engineering, operations research, or related analytics areas.
• Strong data analysis experience with complex, real world datasets.
• Demonstrated capability in solving business problems using both traditional ML and emerging GenAI/LLM based approaches.
• Superior analytical thinking and problem-solving skills.
• Outstanding written and verbal communication skills with confidence in client interactions.