Data Scientist

JOB SUMMARY We are seeking a Data Scientist to support sales enablement initiatives, ideally within the insurance sector. This role involves data preparation, model development, and evaluation for various Group Benefits insurance projects. The successful candidate will manage moderate scope analytics projects, translate data-driven insights into actionable business recommendations, and work with diverse data systems. Key Responsibilities Prepare, clean, and analyze datasets from complex internal data sources for ML/AI features. Utilize LLMs to create features from unstructured data. Design and build segmentation and predictive models for customer and advisor analytics. Own the feature engineering pipeline for ML/AI models. Collaborate with business stakeholders to understand workflows, data requirements, and key performance metrics. Build dashboards and reporting assets to deliver insights to business stakeholders. Contribute to the development and evaluation of modular Gen AI features (e.g., RAG systems, NL-to-SQL, agentic workflows). Develop and implement analytics-enabled solutions that support business goals and process improvement. Deliver complete projects of moderate complexity. Translate analytical findings into business language and recommend solutions to stakeholders and leadership. Document data sources, contribute to structured processes, and support closed-loop tracking for continuous improvement. Participate in daily project updates with the core team. Communicate with business partners to confirm requirements and clarify timeline constraints. Propose and implement technical solutions based on business needs and project deadlines. Engage in hands-on data preparation, analysis, and development tasks. Log all tasks accurately and consistently in Jira. Required Qualifications 35 years of experience as a Data Analyst, Data Scientist, or in a related analytical role. Strong Python skills, including experience with data science libraries (e.g., pandas, NumPy, scikit-learn, PySpark or similar). Strong SQL experience and proficiency with data modeling concepts. Proficiency with BI tools such as Power BI, Tableau, or similar platforms. Demonstrated experience engineering complex features from large, messy, and multi-source datasets and assessing feature quality. Demonstrated experience in end-to-end model development: problem framing, data preparation, feature engineering, model training, validation, and deployment support. Experience with classical statistical methods and ML techniques (e.g., regression, clustering, PCA, decision trees, survival analysis). Ability to translate ambiguous business questions into structured analytical approaches. Curiosity about GenAI and eagerness to learn LLM related workflows, evaluation techniques, and best practices. Strong problem-solving mindset. Solid understanding of ML fundamentals (Exploratory Data Analysis, Feature Engineering, Model Testing). Proficiency with Github and Git. Experience with LLM concepts (Context Engineering, Prompt Engineering, LLM Guardrails). Good communication skills, with the ability to translate complex technical components into simple business requirements. Ability to complete tasks without deviating from scope and timelines, and to independently resolve roadblocks. Preferred Qualifications Experience in an insurance, sales support, or finance environment. Experience with MLOps. Experience with Azure and Databricks. Experience with Agentic AI. Certifications Bachelors degree in Statistics, Math, Computer Science, Engineering, or equivalent technical experience. Education: Bachelors Degree