Senior Data Scientist

Versant Health•Troy, NY

174d•$170,000 - $180,000

About The Position

The Senior Data Scientist is a principal-level individual contributor responsible for architecting, delivering, and governing enterprise-grade advanced analytics and machine learning solutions that influence strategic decisions, financial performance, operational efficiency, and regulatory posture. The role operates across data science, data engineering, cloud analytics platforms, and business strategy, serving as a technical authority for complex analytical initiatives, particularly in regulated healthcare environments. The position is accountable for model development as well as the explainability, stability, auditability, and long-term operational viability of analytical solutions deployed at scale within the Microsoft data ecosystem. It partners with executive stakeholders, data engineering leaders, and platform owners, and guides the work of other data scientists, analysts, and vendors. Associates must comply with HIPAA and company privacy and security requirements when handling protected health information, cardholder data, or other confidential customer information. The role has a fixed annual salary of $170,000 to $180,000 and is not eligible for bonus or incentive compensation.

Requirements

7–10+ years of progressive experience delivering advanced analytics or machine learning solutions in complex, data-rich environments.
Prior experience in healthcare analytics, such as payer, provider, life sciences, or regulated healthcare data domains.
Proven experience deploying production analytics or machine learning solutions supporting mission-critical business processes.
Experience with Python, including pandas, NumPy, scikit-learn, and PyTorch or TensorFlow where appropriate.
Advanced SQL, including T-SQL optimization and analytical patterns.
Knowledge of machine learning explainability and governance techniques, such as SHAP-style approaches and stability testing.
Deep, hands-on, production experience across a sizable portion of the Microsoft analytics ecosystem, including Microsoft Fabric (Lakehouse, Data Warehouse, Real-Time Analytics, and Notebooks), Azure Synapse Analytics (dedicated and serverless SQL pools), Azure Data Factory or Fabric Data Pipelines, Azure Data Lake Storage, Azure Machine Learning (training, deployment, and monitoring), Power BI (semantic models, DAX optimization, and executive dashboards), and Microsoft Purview (cataloging, lineage, and classification).
Compliance with HIPAA and company privacy and security policies and training requirements.

Nice To Haves

Master’s degree in data science, statistics, mathematics, computer science, engineering, or a related field is preferred.
Prior actuarial experience is a plus.

Responsibilities

Architect, design, and deploy advanced statistical, predictive, and machine learning solutions for enterprise decision-making across healthcare, operations, finance, and customer/member domains.
Own the analytical solution lifecycle, including data discovery, feature engineering, modeling, validation, performance monitoring, retraining strategy, and retirement planning.
Serve as a technical authority on model explainability, fairness, robustness, and regulatory defensibility, including in situations with limited ground truth or forward-looking forecasts.
Design analytics solutions within the Microsoft cloud analytics stack in alignment with enterprise data architecture and governance standards.
Partner with executive stakeholders, data engineering leaders, and platform owners to translate business questions into production-ready analytics.
Lead and influence analytics standards without direct authority.
Define best practices for model documentation, audit artifacts, model risk management, stability analysis, reproducibility, and controlled experimentation.
Guide and review work by data scientists, analysts, and vendors to ensure technical rigor and business relevance.
Present analytical findings to senior leadership using clear narratives, decision-oriented visuals, and quantified trade-offs.
Identify analytical risks, data limitations, and downstream impacts of modeling decisions.
Evaluate emerging tools, methodologies, and Microsoft platform capabilities to drive modernization and performance gains.
Comply with HIPAA and company privacy and security requirements when handling protected or confidential information.