Lead, Analytics (Data Science), Product Innovation

Lead Analyst, Product Innovation Job Description Job Summary

The Lead Analyst, Product Innovation drives the application of AI-enabled, data-driven capabilities across the Revenue Cycle Management (RCM) organization. This role is primarily focused on traditional machine learning model development from problem framing and feature engineering through model training, evaluation, and deployment support to improve decision-making and operational performance.

The Lead Analyst partners with product managers, engineers, data scientists, and operational stakeholders to assess business problems, explore feasibility, build prototypes, and evaluate impact. The role requires strong analytical capability, technical proficiency with Python, SQL, and the Azure ecosystem, and the ability to translate complex findings into actionable recommendations. Experience in LLM/GenAI is beneficial (e.g., prompt engineering, RAG, evaluation). Prior RCM industry experience is helpful. Candidates should demonstrate the ability to learn new domains quickly.

Core Competencies & Responsibilities

These representative competencies highlight what matters most for success in this role.

Machine Learning Model Development (Critical)

• Frames business problems into ML use cases; defines target variables, success metrics, and experimental approach.
• Builds, tunes, and evaluates traditional ML models (e.g., regression, classification, tree-based models, time series) and performs feature engineering, model validation, and performance monitoring.

Applied GenAI & LLM Enablement (Important)

• Applies LLM/GenAI capabilities where they add value (e.g., summarization, classification, extraction), including prompt design, RAG basics, and Python-based prototyping.
• Evaluates LLM output quality and risk (accuracy, bias, data leakage), and partners with engineering to translate prototypes into production-ready requirements.

Technical Execution (Python + SQL + Azure)

• Builds prototypes leveraging Python, Azure OpenAI, SQL, and Azure ecosystem tools.
• Partners with engineering and data teams to validate data, refine requirements, and ensure scalable design.

Analytical Problem Solving

• Breaks down complex problems, analyzes root causes, and structures analytical approaches.
• Uses quantitative evidence to form recommendations.

Cross Functional Collaboration

• Works with operations, product, engineering, and AI teams.
• Contributes to requirements, design reviews, and prototype evaluations.

Clear Communication

• Synthesizes complex findings into clear insights, summaries, and stakeholder ready outputs.

Essential Job Functions

• Translate business problems into ML use cases; define target outcomes, evaluation metrics, and baselines.
• Perform data exploration, feature engineering, and dataset creation using Python and SQL; validate data quality and lineage.
• Train, tune, and evaluate traditional ML models; run experiments, compare approaches, and document results and assumptions.
• Partner with engineering and data teams to support deployment patterns (batch/real-time), monitoring, and model performance tracking.
• Prepare stakeholder-ready insights, model performance summaries, and recommendations to inform product direction and prioritization.
• Ensure responsible use of data and models by following internal governance, privacy, and security standards; support documentation and auditability.
  • Where relevant, prototype and evaluate GenAI/LLM components (e.g., prompt iterations, RAG inputs, output evaluation) to complement traditional ML solutions.
• Support cross functional sessions, requirements gathering, and documentation of solution designs.

Desired Experience & Education

• Experience: 3 5 years in analytics or data science roles with hands-on experience building and evaluating traditional ML models in Python (e.g., scikit-learn) using structured data.
  • GenAI/LLMs: Familiarity with LLM applications and evaluation is a plus.
  • Domain: RCM/healthcare experience is helpful; ability to learn workflows and translate them into data/ML problems is expected.
• People Leadership: Not required.
• Education: Bachelor s degree or equivalent experience.
• Preferred Study Areas: Computer Science, Analytics, or related fields.

Numbers of Positions:

2

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