Job SummaryWe are seeking a Data Scientist with experience supporting sales workflows, ideally within the insurance industry, to contribute to data preparation, insight generation, and evaluation activities for our GenAI-powered sales enablement platform. This role blends strong analytical skills with an understanding of advisor and sales team operations, requiring ownership of moderate-scope analytics projects, translation of data into business recommendations, and collaboration across diverse data systems.
Key Responsibilities- Prepare, clean, and analyze datasets for training, validating, and evaluating GenAI/LLM features.
- Collaborate with product, sales, and business stakeholders to understand workflows, data requirements, and performance metrics.
- Build dashboards and reporting assets to track adoption, performance, and business impact.
- Support prompt evaluation, annotation, and quality assurance tasks for AI outputs.
- Contribute to structured knowledge bases, taxonomies, and metadata supporting RAG systems.
- Generate actionable insights to optimize sales processes and improve advisor/end-user experiences.
- Deliver analytics-enabled solutions that support business goals and process improvement.
- Analyze complex datasets and connect data sources across multiple internal systems.
- ranslate analytical findings into business language and recommend solutions to stakeholders.
- Document data sources, contribute to structured processes, and support closed-loop tracking.
- Engage subject matter experts to understand business processes and build collaborative networks.
- Provide guidance and mentorship to junior analysts or data scientists.
Required Qualifications- 3-5 years of experience as a Data Analyst, Data Scientist, or in a related analytical role.
- Strong Python skills.
- Proficiency with BI tools (Power BI, Tableau, or similar).
- Background working with sales datasets; insurance industry exposure is a plus.
- Ability to translate ambiguous business questions into structured analytical approaches.
- Bachelor's degree in Statistics, Math, Computer Science, Engineering, or equivalent technical experience.
- Working knowledge of classical statistical methods (regression, clustering, PCA, decision trees, survival analysis).
- Familiarity with machine learning techniques and AI/ML toolkits.
- Experience navigating large, diverse datasets using structured analytical methods.
- Comfort with data modeling concepts and relational databases.
- Strong communication skills to translate technical insights into business recommendatio ns.
Preferred Qualifications (if any)- Curiosity about GenAI and eagerness to learn LLM workflows, evaluation techniques, and best practices.
- Experience with MLOps, Azure, Databricks, or RAG pipelines.
Certifications (if any)- None required; relevant technical certifications are an asset.
Typical Day- Participate in daily project updates with the core team.
- Communicate with business partners to confirm requirements and timelines.
- Propose and implement technical solutions based on business needs.
- Perform hands-on data preparation, analysis, and development tasks.
- Draft PowerPoint slides outlining solutions for business stakeholders.
- Log tasks accurately in Jira.
- Collaborate with a project team of 4-5 members plus the Data Infrastructure team.
- Report directly to the Project Team Lead.
Candidate Requirements- Must-Have Skills
- Strong problem-solving mindset
- GitHub/Git proficiency
- ML fundamentals (EDA, feature engineering, model testing)
- LLM experience (context engineering, prompt engineering, guardrails)
- Strong communication skills to translate technical concepts into business lang uage
Nice-to-Have Skills- MLOps
- Azure & Databricks
- RAG pipelines
- Years of Experience
- 3-5 years
Degrees/Certifications Required- Bachelor's degree in Statistics, Math, Computer Science, Engineering, or equivalent technical experience
Candidate Disqualifiers- Weak Python skills
- Lack of ownership or initiative
Measures of Success- Ability to accurately assess effort required for tasks without deviating from scope or timelines.
- Effective handling of blockers and escalations with feasible alternatives.
- Delivery of actionable insights and analytics solutions that improve sales workflows.
