AI and Data Science Technical Analyst-Manager

Role Summary

AI and Data Science Technical Analyst-Manager leading technical product management functions within a cross-functional pod of Computational Scientists, Data Scientists, and AI/ML Engineers. Responsible for defining the product vision, strategy, and roadmap for AI-driven solutions and translating complex scientific and business challenges into actionable product plans. Lead a team that uses clinical and real-world data to create innovative data products and ensure delivery of production-ready models and solutions.

Responsibilities

• Product Vision and Strategy: Define and communicate a clear product vision and strategic roadmap for AI and data science initiatives, aligned with EDGE, Digital, R&D and business stakeholders.
• Roadmap and Execution: Manage the entire product lifecycle from ideation to delivery and iteration; break down complex problems into actionable steps for the technical team.
• Cross-Functional Leadership: Lead and collaborate with data scientists, computational scientists, engineers, and MLOps professionals to develop, test, and deploy AI/ML solutions into product teams.
• Stakeholder Management: Serve as the primary contact between the technical team and non-technical stakeholders; translate analytics capabilities and results to a broader audience.
• Requirement Definition: Gather requirements and translate them into technical specifications and user stories.
• Process Management: Implement and oversee agile methodologies to ensure timely delivery of high-quality data products.
• Data and Technology Oversight: Guide the team in leveraging large biomedical datasets (EHRs, clinical trial data, real-world evidence) and adopt a leading-edge tech stack; foster continuous learning.

Qualifications

• Educational Background: Degree in a quantitative/technical field (Computer Science, Engineering, Statistics) or equivalent experience; advanced degrees preferred.
• 8 years of Product Management Experience: Proven experience in a technical PM role, agile environment; track record of launching production-level code and data-driven products.
• Technical Fluency: Knowledge of data science and ML landscape (supervised/unsupervised learning, deep learning, NLP, MLOps); able to discuss technical approaches with engineers and scientists.
• Domain Knowledge: Experience with healthcare data sources (EHRs, clinical trial data, biomedical datasets) preferred.
• Leadership and Communication: Ability to lead cross-functional teams; strong written and verbal communication; data storytelling skills for non-technical audiences.
• Problem-Solving: Passion for solving complex problems with impact on patient outcomes.

Skills

• Familiarity with Python, R, JIRA, Confluence.
• Big data analytics platforms and ML libraries (Databricks, SageMaker, TensorFlow, PyTorch).
• Data visualization tools (Tableau, Plotly, Streamlit).
• Understanding of clinical research processes and pharmaceutical industry contexts.

Education

• Degree in Computer Science, Engineering, Statistics, or related quantitative field; advanced degree preferred.

Additional Requirements

• No explicit travel or physical demands specified as essential in the provided description.