Sr. Analytics Consultant- Data Scientist

EXL

About The Position

Key Responsibilities Operational Metrics & Performance Analysis Develop and maintain operational metrics for in‑home assessment program. Analyze year‑over‑year and period‑over‑period trends to identify performance patterns, risks, and opportunities. Investigate underperforming metrics and identify potential root causes using data‑driven approaches. Partner with stakeholders to define clear success measures and ongoing performance tracking. Data Analysis & Visualization Use SQL, Python and Google BigQuery to access, join, and aggregate member, assessment, and operational datasets. Create clear, accurate visualizations to summarize trends, drivers, and performance gaps. Translate complex analyses into intuitive charts and narratives for both technical and non‑technical audiences. Experimentation & Improvement Initiatives Design and document experiments (e.g., A/B or multivariate tests) to test new approaches aimed at improving operational outcomes Define test and control groups, randomization approaches, and measurement frameworks. Apply statistical methods (e.g., confidence intervals, significance testing) to evaluate experiment results and quantify impact. Synthesize learnings into actionable recommendations for future program improvements. Reporting & Executive Communication Build high‑quality PowerPoint presentations to communicate findings, insights, and recommendations to executive leaders. Clearly explain results, tradeoffs, and next steps using data‑backed storytelling. Provide regular written updates summarizing progress, key metrics, risks, and outcomes. Collaboration & Ways of Working Partner with internal analytics, operations, and business stakeholders to align analyses with program priorities. Support clear documentation and handoffs for metrics, experiments, and analytical methodologies. Use AI and agent tools (e.g., Cursor IDE or VS Code) responsibly to accelerate analysis, coding, and documentation while validating outputs.

Requirements

SQL/Python – Write and maintain SQL/Python to analyze operational and member data; build repeatable queries for metrics, trend analysis, and experimentation.
Google BigQuery – Run queries, access large datasets, and join and aggregate data to support analysis, reporting, and experimentation.
Excel (Moderate) – Maintain tracking sheets, document logic, and support analysis outputs.
Stakeholder coordination – Partner with internal teams and external stakeholders; clarify analytical scope and ensure alignment on metrics and findings.
Impact tracking and reporting – Define and monitor performance metrics; summarize insights and trends over time.
Statistics – Apply basic statistical concepts (distributions, confidence intervals, significance testing) to validate findings and interpret results.
Experimental design – Design and document experiments, including test/control definitions, randomization, and measurement approaches.
Machine learning (foundational) – Understand core ML concepts (e.g., propensity or response models, scoring) and interpret model outputs when applicable.
AI and agent tools (Cursor / VS Code) – Use AI assistants responsibly to improve productivity in coding, analysis, and documentation.
Specification writing – Clearly document analytical logic, assumptions, and methodologies.
Presentation skills – Build clear, executive‑ready PowerPoint presentations and written updates that communicate insights and recommendations effectively.

Nice To Haves

Experience with healthcare, in‑home assessments, or risk adjustment / HCC‑related analytics.
Experience presenting directly to executive or senior leadership audiences.
Familiarity with operational analytics in large, complex programs.

Responsibilities

Develop and maintain operational metrics for in‑home assessment program.
Analyze year‑over‑year and period‑over‑period trends to identify performance patterns, risks, and opportunities.
Investigate underperforming metrics and identify potential root causes using data‑driven approaches.
Partner with stakeholders to define clear success measures and ongoing performance tracking.
Use SQL, Python and Google BigQuery to access, join, and aggregate member, assessment, and operational datasets.
Create clear, accurate visualizations to summarize trends, drivers, and performance gaps.
Translate complex analyses into intuitive charts and narratives for both technical and non‑technical audiences.
Design and document experiments (e.g., A/B or multivariate tests) to test new approaches aimed at improving operational outcomes
Define test and control groups, randomization approaches, and measurement frameworks.
Apply statistical methods (e.g., confidence intervals, significance testing) to evaluate experiment results and quantify impact.
Synthesize learnings into actionable recommendations for future program improvements.
Build high‑quality PowerPoint presentations to communicate findings, insights, and recommendations to executive leaders.
Clearly explain results, tradeoffs, and next steps using data‑backed storytelling.
Provide regular written updates summarizing progress, key metrics, risks, and outcomes.
Partner with internal analytics, operations, and business stakeholders to align analyses with program priorities.
Support clear documentation and handoffs for metrics, experiments, and analytical methodologies.
Use AI and agent tools (e.g., Cursor IDE or VS Code) responsibly to accelerate analysis, coding, and documentation while validating outputs.