Staff Data Scientist | Analytics

What You'll Do

• Independently partner with business stakeholders to understand complex operational challenges, build domain context, and frame analytical approaches — without predefined solutions or heavy direction
• Perform deep exploratory data analysis across payment operations, chart review workflows, and customer performance metrics to diagnose root causes and identify optimization opportunities
• Build and maintain dashboards and reporting infrastructure that give leaders real-time visibility into operational and financial performance
• Conduct business reviews including month-to-month bridges, waterfall analyses, and price-volume-mix decompositions to explain performance variance
• Use LLMs and AI-assisted tools (e.g., Claude, GPT) for data exploration, pattern identification, and accelerating analytical workflows
• Translate ambiguous business questions into structured analytical approaches with clear, actionable recommendations
• Work with both models and querying to understand problems — diagnosing first, then standardizing and building scalable solutions
• Partner cross-functionally with Product, Data Science, Engineering, and Operations teams to deliver analytics-ready solutions
• Build foundational data assets — base data sets, aggregates, and pipelines — required for robust reporting and insight generation

What You Bring

• 4-6 years of experience in product analytics OR a high-level business analytics role (quantifying product/business impact, translating data/insights/KPI into business decisions, showing impact using data and analysis, translating complex data analysis into stories to influence business decisions, etc.)
• Strong proficiency in SQL and Python for data manipulation, analysis, and visualization
• Demonstrated ability to independently diagnose complex business problems through exploratory data analysis — comfortable working without predefined solutions
• Experience with BI/visualization tools (e.g., Power BI, Tableau, Looker) and building dashboards for business stakeholders
• Familiarity with LLMs and AI tools for data analysis — comfortable using AI-assisted workflows to accelerate insight generation and excited to push the boundaries of what's possible
• Degree in a quantitative field (e.g., Economics, Statistics, Operations Research, Data Science, Engineering, or related discipline)
• Strong communication and storytelling skills — ability to work directly with senior stakeholders and present complex findings clearly to both technical and non-technical audiences
• Experience working with large-scale data warehouses (Databricks, Snowflake, BigQuery, or similar)
• Consulting mindset: ability to quickly build business context in unfamiliar domains, structure ambiguous problems, and deliver actionable recommendations
• Curiosity, growth mindset, and excitement about learning new areas — including healthcare, LLMs, and GenAI
• Comfort operating in a fast-paced, ambiguous environment and balancing short-term deliverables with longer-term strategic work

Nice-to-Haves

• Background in management consulting or strategic advisory (McKinsey, BCG, Bain, Deloitte, or similar)
• Economics background with understanding of causal reasoning and business impact measurement
• Experience in healthcare, payment integrity, or health insurance operations (or strong aptitude for quickly learning complex regulated domains)
• Experience with financial modeling and simulation — quantifying the impact of operational changes
• Familiarity with event-stream analytics and user behavior analysis
• Experience with dbt, Airflow, or similar data pipeline tools