AI and Data Science Technical Analyst-Manager

Role Summary

Location: Cambridge, MA. We are seeking an AI and Data Science Technical Analyst/Manager to lead technical product management functions in a cross-functional pod of computational scientists, data scientists, and AI/ML engineers. You will define the product vision, strategy, and roadmap for AI-driven solutions, translating complex scientific and business challenges into actionable plans. You will orchestrate a team that uses clinical and real-world data to create innovative data products and production-ready models that deliver a competitive advantage.

Responsibilities

• Product Vision and Strategy: Define and communicate a clear product vision and strategic roadmap for AI and data science initiatives, aligning with EDGE, Digital, R&D and business stakeholders.
• Roadmap and Execution: Manage the entire product lifecycle from ideation and scoping to delivery and iteration; break down complex problems into actionable steps for the technical team.
• Cross-Functional Leadership: Lead and collaborate with a team of 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 point of contact between the technical team and non-technical stakeholders, including researchers, clinicians, and business unit leaders; translate and champion the team's analytics capabilities and results to a broader audience.
• Requirement Definition: Work with stakeholders to identify areas for data-driven improvement, gathering requirements and translating them into technical specifications and user stories for the development team.
• Process Management: Implement and oversee agile methodologies to manage workflow and ensure the timely delivery of high-quality data products, from preparing common data models to deploying production-ready models.
• Data and Technology Oversight: Guide the team in leveraging diverse and large-scale biomedical datasets (e.g., EHRs, clinical trial data, and real-world evidence); ensure adoption of a leading-edge tech stack and foster continuous learning.

Qualifications

• Required: Educational Background: A degree in a quantitative or technical field such as Computer Science, Engineering, Statistics, or significant experiences in technical/digital industries, preferably with advanced degrees.
• Required: 8 years of Product Management Experience: Proven experience in a technical product management role, preferably in an agile environment, with a track record of launching production-level code and data-driven products.
• Required: Technical Fluency: Strong understanding of the data science and machine learning landscape; familiarity with supervised/unsupervised learning, deep learning, NLP, and MLOps. Able to discuss technical approaches and challenges with engineers and scientists.
• Required: Domain Knowledge: Experience with healthcare data sources, such as electronic health records (EHRs), clinical trial data, or other biomedical datasets is highly preferred.
• Required: Leadership and Communication: Demonstrated ability to lead cross-functional teams and collaborate effectively; excellent written and verbal communication skills with data storytelling ability.
• Required: Problem-Solving Skills: A passion for solving complex problems with a desire to make a tangible impact on patient outcomes.
• Preferred: Familiarity with data science languages and tools such as Python, R, JIRA, Confluence.
• Preferred: Knowledge of big data analytics platforms and ML libraries (e.g., Databricks, SageMaker, TensorFlow, PyTorch).
• Preferred: Experience with data visualization tools like Tableau, Plotly, or Streamlit.
• Preferred: Experience in software/digital development and pharmaceutical industries and a solid understanding of the clinical research process.

Skills

• Python
• R
• JIRA
• Confluence
• Databricks
• SageMaker
• TensorFlow
• PyTorch
• Tableau
• Plotly
• Streamlit
• Clinical research process familiarity in software/digital development for pharma

Education

• Educational Background: A degree in a quantitative or technical field such as Computer Science, Engineering, Statistics, or significant experiences in technical/digital industries, preferably with advanced degrees.