Data Scientist

Data Scientist – Insurance Analytics\nRole Summary\nWe are seeking a Senior Data Scientist (6–7 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.\nThe 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.\nKey Responsibilities\nPerform exploratory data analysis (EDA), feature engineering, and hypothesis testing to identify fraud patterns and anomalies.\nBuild, evaluate, and optimize traditional statistical models as well as tree-based ML models such as Random Forest, XGBoost, CatBoost, and LightGBM.\nExplore and apply LLM based approaches (e.g. text classification, summarization, entity extraction) for leveraging unstructured data such as claim notes, adjuster comments and documents.\nDevelop GenAI powered accelerators for documentation, feature ideation, data enrichment and model insight generation.\nIndependently conduct data analysis, research, model experimentation and translate findings into actionable insights.\nWrite clean, efficient and production ready code using Python and SQL.\nWork extensively with large datasets using cloud platforms, primarily Google Cloud Platform (GCP).\nQuery and manage data using Big Query and datasets stored in Cloud Storage (Buckets).\nUse Git for version control, collaboration and code review.\nPrepare clear, concise and impactful presentations for clients, explaining analytical findings to both technical and nontechnical stakeholders.\nCollaborate with business, data engineering, and client teams to ensure models align with investigation strategies and broader business objectives\nRequired Skills & Experience\n5–11years of hands on experience in data science, analytics, or applied machine learning\nStrong understanding of statistical modeling, probability concepts, and hypothesis testing\nProven experience with tree-based and ensemble machine learning models (RF, XGBoost, CatBoost, LightGBM)\nExperience working with unstructured data and NLP techniques, preferably including LLMs (OpenAI, Gemini, Llama, etc.)\nPractical exposure to GenAI workflows such as prompt engineering, fine tuning, retrieval augmented generation (RAG), or automated insight generation\nExpert‑level SQL for data extraction, transformation, and analysis\nStrong Python skills for data analysis, machine learning, and LLM based pipelines\nExperience using Git for source code management\nSolid exposure to cloud based analytics environments, preferably Google Cloud Platform (GCP), Big Query, and Cloud Storage\nAbility to work independently, manage deliverables and drive tasks end to end.\nExcellent verbal and written communication skills, essential for a client facing role.\nCandidate Profile\nBachelor’s/Master’s degree in economics, statistics, mathematics, computer science/engineering, operations research, or related analytics areas.\nStrong data analysis experience with complex, real world datasets.\nDemonstrated capability in solving business problems using both traditional ML and emerging GenAI/LLM based approaches.\nSuperior analytical thinking and problem-solving skills.\nOutstanding written and verbal communication skills with confidence in client interactions.