Associate Director, Enterprise Data Science (Staff Data Scientist)
Alkermes
1 month ago
Remote friendly (Greater Boston)
United States
IT
Responsibilities
- Support and scale data science use cases: develop/deploy analytical models; implement agentic AI and predictive ML; build operations planning/optimization pilots using operations research (validation, impact, scalable deployment); scale PoCs into maintainable production software services with production-ready code and DevOps.
- Engage stakeholders: partner with cross-functional teams; translate business challenges into high-impact AI/ML use cases; run discovery/problem-framing to identify root causes and prioritize measurable value; collaborate with domain experts; communicate insights for technical and non-technical audiences; drive change management and adoption.
- Lead data science capabilities: design/build production-grade digital tools, reusable frameworks, and scalable AI/ML platforms; lead high-impact initiatives (scope, timelines, resourcing); evangelize ML/AI adoption; integrate emerging technologies; track/report progress; champion FAIR principles.
- Own digital products: manage end-to-end lifecycle; ensure reliability/scalability/business relevance; mentor and develop talent.
Qualifications
- Education: Master’s in Data Science, Operations Research, Industrial Engineering, CS, Statistics, or related (Ph.D. preferred).
- Experience: 10+ years in advanced analytics/OR/ML/AI with measurable impact; pharma operations experience strongly preferred.
- Required skills: Python and SQL; ML/AI libraries (e.g., pandas, NumPy, scikit-learn); MLOps end-to-end; AWS; data pipelines/data governance/engineering best practices.
- Strong plus: optimization tools (OR-Tools, Gurobi, Pyomo); Snowflake; GenAI/LLMs; DBT.
- Preferred: pharma/biotech/healthcare; regulatory/compliance (e.g., HIPAA); GenAI simulation modeling; hybrid Waltham schedule.
Compensation/Benefits
- Annual base salary: $158,000–$180,000; annual performance pay bonus; competitive benefits package.
- Support and scale data science use cases: develop/deploy analytical models; implement agentic AI and predictive ML; build operations planning/optimization pilots using operations research (validation, impact, scalable deployment); scale PoCs into maintainable production software services with production-ready code and DevOps.
- Engage stakeholders: partner with cross-functional teams; translate business challenges into high-impact AI/ML use cases; run discovery/problem-framing to identify root causes and prioritize measurable value; collaborate with domain experts; communicate insights for technical and non-technical audiences; drive change management and adoption.
- Lead data science capabilities: design/build production-grade digital tools, reusable frameworks, and scalable AI/ML platforms; lead high-impact initiatives (scope, timelines, resourcing); evangelize ML/AI adoption; integrate emerging technologies; track/report progress; champion FAIR principles.
- Own digital products: manage end-to-end lifecycle; ensure reliability/scalability/business relevance; mentor and develop talent.
Qualifications
- Education: Master’s in Data Science, Operations Research, Industrial Engineering, CS, Statistics, or related (Ph.D. preferred).
- Experience: 10+ years in advanced analytics/OR/ML/AI with measurable impact; pharma operations experience strongly preferred.
- Required skills: Python and SQL; ML/AI libraries (e.g., pandas, NumPy, scikit-learn); MLOps end-to-end; AWS; data pipelines/data governance/engineering best practices.
- Strong plus: optimization tools (OR-Tools, Gurobi, Pyomo); Snowflake; GenAI/LLMs; DBT.
- Preferred: pharma/biotech/healthcare; regulatory/compliance (e.g., HIPAA); GenAI simulation modeling; hybrid Waltham schedule.
Compensation/Benefits
- Annual base salary: $158,000–$180,000; annual performance pay bonus; competitive benefits package.