Data Scientist

Job description

About the job Role Overview: Design, develop, and implement Machine Learning, AI, and Generative AI solutions that support measurable business outcomes across the disability insurance value chain, including fraud detection, duration modelling, risk scoring, and next-best-action recommendations. Work closely with business stakeholders, actuarial teams, claims teams, and senior data scientists to understand business problems and translate them into practical analytics and ML use cases. Contribute across the analytics lifecycle, including problem framing, exploratory data analysis, feature engineering, model development, validation, deployment support, and performance monitoring. Support technical problem solving within project teams and share knowledge with analysts and junior team members where required. Assist in developing experimentation frameworks, analytical approaches, and reusable assets that improve model quality and accelerate AI adoption. Follow responsible AI principles, model governance standards, data privacy requirements, and regulatory guidelines while developing analytics solutions. Design, develop, and optimize ML models for claim segmentation, risk scoring, and outcome prediction Build NLP pipelines for medical records, claim notes, and unstructured documents Perform feature engineering using structured and unstructured claims data Conduct model validation, performance tuning, bias detection, and explainability analysis Support model deployment, monitoring, retraining, and drift detection Collaborate with business and actuarial & claims teams to translate objectives into ML use cases The candidate will be part of an agile analytics team that combines diverse skills, backgrounds, and perspectives to solve complex business problems. A suitable candidate should have 4+ years of applied data science experience, preferably with exposure to healthcare, insurance, disability claims, or other regulated domains. Technical Skillsets: Strong hands-on proficiency in Python and commonly used machine learning libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Good understanding of machine learning and predictive analytics techniques, including classification, regression, survival analysis, feature engineering, model tuning, and explainability methods. Hands-on exposure to Natural Language Processing and Deep Learning techniques for structured and unstructured data, including text classification, information extraction, or document analytics. Practical exposure to Generative AI and Large Language Models, including prompt engineering, embeddings, Retrieval-Augmented Generation, vector databases, and model evaluation. Working knowledge of MLOps and model lifecycle practices, including model deployment support, monitoring, drift detection, retraining, and tools such as MLflow, Airflow, Docker, Kubernetes, or cloud-native ML platforms. Strong SQL and data manipulation skills with the ability to work with large structured and unstructured datasets. Good understanding of relational databases, data modeling concepts, and data architecture fundamentals. Ability to apply structured problem-solving, analytical thinking, and ownership while designing scalable data science solutions. Exposure to Group Insurance, healthcare, disability insurance, or claims analytics will be preferred. Basic understanding of insurance data structures, such as policies, insured members, coverages, claims, and related entities, will be an advantage. Understanding of healthcare or disability insurance processes, including claims adjudication, utilization management, or provider analytics, will be an added advantage. Experience collaborating with cross-functional teams and supporting stakeholder discussions in a project environment. Ability to communicate analytical findings and model outputs clearly to both technical and non-technical stakeholders. Candidate Profile: Bachelor’s/master’s degree in computer science/engineering, Data Science, Mathematics, or related analytics areas are welcome to apply 4+ years of hands-on experience in Data Science, Machine Learning, or AI solution development, with experience delivering analytical solutions that support business outcomes. Exposure to healthcare, insurance, disability claims, or other regulated industries will be preferred. Ability to design and implement practical data science solutions using structured and unstructured data Superior analytical and problem-solving skills Outstanding written and verbal communication skills Able to work in a fast-paced, continuously evolving environment and take ownership of challenging analytical tasks Can understand cross cultural differences and can work with clients across the globe