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.

• 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.

• 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, Sage Maker, Tensor Flow, 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.

• Python
• R
• JIRA
• Confluence
• Databricks
• Sage Maker
• Tensor Flow
• Py Torch
• Tableau
• Plotly
• Streamlit
• Clinical research process familiarity in software/digital development for pharma

• 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.