Senior Data Scientist – Finance & Risk Analytics

Role Overview

We are seeking a Senior Data Scientist – Finance & Risk Analytics to lead the development of advanced analytics and AI solutions that improve decision intelligence, operational efficiency, and risk management across Oncology & Multispecialty (O&M) Finance.

This role is a hands‑on senior individual contributor position. The Senior Data Scientist will serve as the core model builder and analytical executor, translating high‑priority finance use cases into production‑ready models, insights, and decision support tools. The role balances deep technical expertise with strong business context and close partnership with Finance stakeholders.

The ideal candidate thrives in applied, real‑world environments, delivers results quickly, and is comfortable working in audit‑sensitive, SOX‑aligned finance contexts where accuracy, explainability, and trust are critical.

Key Responsibilities

Advanced Analytics & AI Execution

• Lead the design and execution of 1–2 high‑priority O&M Finance use cases, such as:
• Fraud & Risk Management
• Financial Controller / Decision Intelligence
• Agentic AI - enabled analytics
• Forecasting and Scenario Modeling
• Collections Improvement Analytics
• Develop and deploy:
• Time‑series forecasting models (short‑term, long‑term, sparse data)
• Anomaly detection and risk models (transactions, journals, contracts, pricing)
• Scenario and sensitivity models to support finance decision‑making
• Document intelligence and entity extraction from contracts and financial documents
• Agentic or LLM‑augmented workflows where appropriate
• Explore upstream data sources and data products; assess data readiness, quality, and limitations
• Engineer features from transactional, contractual, and time‑series financial data
• Apply strong statistical rigor to validate model performance and quantify improvements over legacy or rules‑based methods
• Partner closely with ML Engineering to productionize models, including monitoring, drift detection, retraining strategies, and operational reliability
• Collaborate with the Technical Product Manager and Lead Data Scientist to define scope, success metrics, and iterative delivery plans
• Ensure model explainability, transparency, and auditability, particularly for SOX, finance, and risk use cases

Stakeholder Engagement

• Translate business requirements and finance problems into clear analytical approaches and modeling logic
• Work directly with Finance SMEs (e.g., Controllers, Risk, FP&A, Operations) to:
• Validate assumptions
• Interpret results
• Incorporate real‑world financial constraints into models
• Build strong, trust‑based relationships with stakeholders across:
• Finance and business units
• Data Engineering and ML teams
• Product and leadership groups
• Support UAT and post‑deployment validation, resolving issues in a structured and well‑communicated manner

Communication & Documentation

• Communicate progress, risks, dependencies, and outcomes to product leadership
• Proactively raise concerns and seek leadership support when needed
• Create and maintain high‑quality documentation, including:
• Technical design and modeling documentation
• Validation and performance summaries
• Release notes and user guidance for business audiences

Minimum Qualifications

• Bachelor’s degree or equivalent experience
• 7+ years of professional experience in progressively advanced data science or applied AI/ML roles
• Proven experience as a hands‑on model builder delivering end‑to‑end analytical solutions

Critical Skills & Experience

Strong background in:

• Forecasting
• Classification
• Anomaly detection and risk modeling
• Statistical modeling and validation

Demonstrated experience working with financial data, such as:

• AR / AP
• General Ledger (GL)
• Contracts
• Transactional finance data

Advanced proficiency in Python and SQL

Solid understanding of:

• Modern data and digital architectures
• APIs and cloud platforms
• Data ecosystems and software development best practices
• Ability to clearly explain model outputs and insights to non‑technical and finance‑focused audiences

Preferred / Additional Skills

• Experience implementing agentic workflows and/or LLM‑augmented analytics
• Familiarity with SOX, audit, or financial controls environments
• Prior experience in healthcare, life sciences, or complex B2B finance
• Experience with Power BI or similar BI/visualization tools
• Exposure to ERP or financial systems (e.g., SAP, PeopleSoft)

Working Conditions:

• In office requirement, we are Flex and Connect with 2 days a week in office