Lead Insurance Data Scientist - Predictive Analytics

Our client, a prominent player in the insurance sector, is looking for a Lead Insurance Data Scientist to spearhead their predictive analytics initiatives in Kochi, Kerala, IN . This on-site role is critical to enhancing our client's ability to understand risk, optimize pricing, detect fraud, and improve customer retention. As the Lead Data Scientist, you will guide a team of data scientists and analysts in developing and deploying sophisticated statistical models and machine learning algorithms. Your responsibilities will encompass the entire data science lifecycle, from data acquisition and preprocessing to model building, validation, and implementation. You will work closely with actuarial teams, underwriting departments, and claims processing units to identify key business questions and translate them into data-driven solutions. The ideal candidate will possess a deep understanding of insurance products, regulatory environments, and common data challenges within the industry. Proven experience in developing predictive models for areas such as customer lifetime value, fraud detection, risk assessment, and claims frequency/severity is essential. Strong expertise in programming languages like Python or R, along with proficiency in SQL and experience with big data technologies (e.g., Spark, Hadoop), are required. Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data visualization tools is also important. You should have excellent leadership qualities, the ability to mentor junior team members, and strong communication skills to effectively present complex findings to non-technical stakeholders. A track record of successfully implementing data science solutions that have delivered measurable business impact is highly valued. If you are passionate about leveraging data to revolutionize the insurance landscape and possess the technical and leadership skills to drive innovation, we encourage you to apply.

Key Responsibilities:

Lead the development and implementation of advanced predictive models for insurance. Mentor and manage a team of data scientists and analysts. Collaborate with business stakeholders to define analytical objectives and strategies. Design and execute data mining and statistical analysis projects. Build, validate, and deploy machine learning models for risk, pricing, fraud, and customer analytics. Ensure data quality and integrity throughout the analysis process. Communicate complex analytical findings to both technical and non-technical audiences. Stay current with the latest advancements in data science and insurance analytics. Contribute to the strategic direction of data science within the organization. Optimize existing models for performance and accuracy.

Qualifications

Master's or Ph.D. in Statistics, Data Science, Mathematics, Computer Science, or a related quantitative field. 7+ years of experience in data science, with a significant focus on the insurance industry. Proven experience in leading data science projects and teams. Expertise in statistical modeling, machine learning, and predictive analytics techniques. Proficiency in Python or R, and SQL. Experience with big data technologies (Spark, Hadoop). Familiarity with machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Strong understanding of insurance products and industry challenges. Excellent problem-solving, analytical, and communication skills. Experience with data visualization tools.