Senior Data Scientist
Explicitly requires experience with vibe coding tools (Cursor, Claude Code, Windsurf); expects use of AI-assisted coding workflows.
About the Role
Senior Data Scientist role to design, develop, and deploy applied machine learning solutions on large-scale healthcare data. Own the end-to-end model lifecycle, ensure interpretability and compliance, and provide technical leadership and mentorship across cross-functional teams.
Job Description
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.