Senior Associate - Lead Data Scientist

Execute AI/ML and GenAI initiatives in partnership with data, technology, product and business teams. Deliver business-aligned OKRs, ensuring AI initiatives are outcome-driven and tied to enterprise value creation. Deliver major portions of Model Development Life Cycle (MDLC) from data exploration and feature engineering to model training, validation, deployment, and performance monitoring.

Develop and help operate models using cloud platforms (e.g., AWS, GCP) and modern data platforms (Snowflake, Databricks), ensuring quality, security, and scalability, partnering with ML engineers for large-scale production deployment and scaling.

Assist in designing and evaluating agentic/AI-powered solutions that automate routine business tasks with human-in-the-loop checkpoints, escalation thresholds, and safety guardrails. Implement and refine LLM/RAG approaches (vector stores, embeddings, retrieval optimization, prompt orchestration) with evaluators for reliability.

Work with AI Engineers to ensure AI capabilities are accessible through APIs, dashboards, and integrated workflow applications used by business teams. Build lightweight UI prototypes (e.g., Streamlit) to validate usability and support adoption.

Adhere to model governance, documentation, testing, and CI/CD best practices in partnership with MLOps. Stay informed on industry trends by engaging in relevant conferences and sharing key insights with internal and external audiences as appropriate. This role involves limited travel (<10%) to events and vendor meetings.

Advanced degree in Computer Science, Data Science, Machine Learning, AI, Engineering, Mathematics, Statistics, or a related quantitative field. 5+ years of experience applying data science and AI/ML to real-world business problems. Ability to communicate complex ideas simply, presenting impact, trade-offs, and recommendations to non-technical partners.

Proficiency in Python and SQL; working knowledge of core software engineering concepts (version control with Git/GitHub, testing, logging). Experience with cloud platforms like AWS/GCP services (e.g., SageMaker, Bedrock, Vertex AI) and modern data ecosystems (Snowflake/Databricks). Working knowledge of LLMs, RAG architecture, and agentic frameworks, including safe automation design and evaluation practices.

Solid grounding in ML methods (supervised and unsupervised learning, gradient boosting, exposure to deep learning), feature engineering, regularization, cross-validation. Ability to translate analytical findings into clear business insights and collaborate effectively with cross-functional partners. Experience in mentoring junior data scientists is a plus.

Experience in Life Insurance industry or consumer finance domains is a plus.