Senior Data Scientist

Role

Lead the design, development, and deployment of applied machine learning solutions for complex business and clinical problems using large-scale healthcare data. Own the end-to-end model lifecycle, from problem framing and feature engineering to deployment and post-production monitoring, while ensuring interpretability, explainability, and compliance with governance and regulatory standards.

Key Responsibilities

• Lead end-to-end ML solution design, development, evaluation, validation, deployment, and monitoring.
• Develop and review production-grade Python code following software engineering best practices (testing, modularization, version control, CI/CD).
• Architect scalable data science workflows using Python, SQL, and distributed data processing frameworks in cloud or enterprise environments.
• Apply and advance classical ML, deep learning, time-series modeling, and survival analysis techniques as required.
• Ensure models are interpretable, explainable, and compliant with enterprise governance and ethical standards (bias, fairness, auditability).
• Partner with engineering, product, clinical, and business stakeholders to translate ambiguous problems into actionable analytical solutions.
• Provide technical leadership and mentorship to senior and mid-level data scientists; set standards for modeling, experimentation, and code quality.
• Review and approve modeling approaches and influence architectural and methodological decisions.
• Communicate insights, risks, and tradeoffs clearly to both technical and executive audiences.

Required Qualifications

• 7+ years building production-quality, maintainable, and testable code.
• 7+ years in machine learning and statistical modeling fundamentals, including feature engineering, model training/tuning/evaluation, and interpretability techniques (e.g., SHAP).
• Hands-on experience with deep learning architectures where appropriate.
• Strong applied experience with time-series analysis and survival analysis.
• Experience with vibe coding tools such as Cursor, Claude Code, and Windsurf.
• 7+ years of healthcare data literacy with claims, EHR, lab, and pharmacy data and familiarity with coding systems (ICD, CPT, NDC, SNOMED, LOINC) and interoperability standards (FHIR, HL7).
• 7+ years leading complex applied data science initiatives from concept to production and mentoring senior data scientists.
• Advanced proficiency in Python for data science and ML (Pandas, NumPy, scikit-learn, PyTorch or equivalent) and advanced SQL skills for complex data transformations.

Preferred Qualifications

• Experience with MLOps practices (deployment, monitoring, retraining, drift detection).
• Prior experience in regulated or highly governed environments.
• Familiarity with cloud platforms and distributed computing (e.g., Spark, Databricks, AWS, GCP, Azure).

Soft Skills

• Excellent written and verbal communication; ability to convey technical concepts to non-technical stakeholders.
• Ability to reason about data quality, missingness, bias, and confounding in healthcare datasets.

Work Location & Other

• Telecommute from anywhere within the U.S. (subject to company telecommuter policy).
• Compensation range provided in posting.