Associate Director, AI/ML Engineering
Alnylam Pharmaceuticals
1 month ago
Remote friendly (Cambridge, MA)
United States
IT
Key Responsibilities:
- Provide people leadership for a small team of 2 to 4, including mentoring, skill development, performance coaching, and career growth planning.
- Maintain oversight responsibility for the Omnichannel & Media Optimization and Evaluation & Learning Value Teams; serve as a primary point of contact for leadership alignment, prioritization, and delivery commitments.
- Use domain expertise to translate business strategy into decision-product roadmaps, analytic requirements, and measurable outcomes.
- Work across multiple Value Teams to deliver reusable ML capabilities that scale across franchises and markets.
- Own AI/ML engineering strategy and roadmap for assigned decision products and shared platforms; sequence releases to maximize business impact, adoption, and reliability.
- Productionize predictive and prescriptive models powering prioritization, triggers, recommendations, and optimization with outputs embedded into core workflows.
- Own model lifecycle management: training pipelines, validation, deployment, monitoring, drift detection, retraining triggers, rollback plans, and ongoing support operating model.
- Build feature pipelines and model inputs aligned to gold-layer data products and single source of truth metrics; enforce disciplined versioning to prevent logic drift.
- Partner with Advanced Analytics to define deployable requirements, evaluation approaches, and measurement plans.
- Partner with Data Stewardship for traceability, governance, and approved metric/feature reuse pathways.
- Use AI tooling (AI-native IDEs and safe agentic AI analytics) and operate in agile rhythms using Jira and GitHub-based version control.
Qualifications:
- BS in Computer Science, Engineering, Data Science, Information Systems, or related field (or equivalent experience).
- Minimum 12 years in software engineering, ML engineering, or applied data science; ideally healthcare/life sciences.
- Experience leading/developing teams, including mentoring and career growth.
- Strong software engineering fundamentals: testing, code reviews, CI/CD, operational support; ability to lead standards.
- Experience with modern ML frameworks/APIs, model evaluation, production monitoring, and ML governance (traceability, reproducibility, explainability in regulated environments).
- Advanced SQL; comfort with Spark and dbt for analytics engineering and metric standardization.
- Experience using AI tooling to improve throughput and quality (responsible agentic AI analytics; AI-native IDEs).
- Ability to communicate technical tradeoffs and drive alignment across functions/Value Teams.
- Experience working with Jira and git-based version control in GitHub.
- Provide people leadership for a small team of 2 to 4, including mentoring, skill development, performance coaching, and career growth planning.
- Maintain oversight responsibility for the Omnichannel & Media Optimization and Evaluation & Learning Value Teams; serve as a primary point of contact for leadership alignment, prioritization, and delivery commitments.
- Use domain expertise to translate business strategy into decision-product roadmaps, analytic requirements, and measurable outcomes.
- Work across multiple Value Teams to deliver reusable ML capabilities that scale across franchises and markets.
- Own AI/ML engineering strategy and roadmap for assigned decision products and shared platforms; sequence releases to maximize business impact, adoption, and reliability.
- Productionize predictive and prescriptive models powering prioritization, triggers, recommendations, and optimization with outputs embedded into core workflows.
- Own model lifecycle management: training pipelines, validation, deployment, monitoring, drift detection, retraining triggers, rollback plans, and ongoing support operating model.
- Build feature pipelines and model inputs aligned to gold-layer data products and single source of truth metrics; enforce disciplined versioning to prevent logic drift.
- Partner with Advanced Analytics to define deployable requirements, evaluation approaches, and measurement plans.
- Partner with Data Stewardship for traceability, governance, and approved metric/feature reuse pathways.
- Use AI tooling (AI-native IDEs and safe agentic AI analytics) and operate in agile rhythms using Jira and GitHub-based version control.
Qualifications:
- BS in Computer Science, Engineering, Data Science, Information Systems, or related field (or equivalent experience).
- Minimum 12 years in software engineering, ML engineering, or applied data science; ideally healthcare/life sciences.
- Experience leading/developing teams, including mentoring and career growth.
- Strong software engineering fundamentals: testing, code reviews, CI/CD, operational support; ability to lead standards.
- Experience with modern ML frameworks/APIs, model evaluation, production monitoring, and ML governance (traceability, reproducibility, explainability in regulated environments).
- Advanced SQL; comfort with Spark and dbt for analytics engineering and metric standardization.
- Experience using AI tooling to improve throughput and quality (responsible agentic AI analytics; AI-native IDEs).
- Ability to communicate technical tradeoffs and drive alignment across functions/Value Teams.
- Experience working with Jira and git-based version control in GitHub.