Associate Dir, Full Stack Data Scientist

Associate Dir, Full Stack Data Scientist Purpose

The purpose of the Associate Director is to lead and deliver cross‑functional Data Science capabilities enablement as a Full Stack Data Scientist in support of R&D Data Science needs for Bayer’s Pharmaceutical division. You will coordinate the activities that are needed and be hands‑on in delivery of analytics stacks for R&D as part of the DSAI-DDRC team. The technology stack includes platforms, applications, integration tools, and analytics tools supporting processes within R&D.

Additionally, you will coordinate the work conducted by external analytics developers and process capability stack operations, providing expert guidance to product team members to strategize, deliver, and execute on enablement tasks. This includes managing issue analysis, proper triage and issue assignments, and planning/communication of timelines and deployments in support of R&D DSAI.

The primary location for this role can be either Cambridge, MA or Whippany, NJ. Remote work arrangements will not be accommodated for this role.

• Accountable to ensure the entire DS capabilities stack for the assigned process and tasks are effective, efficient, and meeting business operational needs;
• Hands on development of capabilities as required by the initiatives, mission leads, and product owners and other DSAI accountable parties;
• Identify business technology developments in the industry early on and actively promote them within the organization;
• Responsible for providing conceptual guidance to team members in appropriately designing certain capabilities as required to support the DSAI Analytics strategy;
• Ensure assigned business process area technology needs are met by interfacing and collaborating with the R&D functions; preparing and supporting delivery of DS capabilities and roadmaps in close collaboration with Head of DDRC and DSAI sub‑clusters;
• Accountable to ensure process area capability issues are assessed upstream and downstream, communicated, coordinated and resolved promptly;
• Design, develop, and maintain full stack applications to support R&D initiatives;
• Collaborate with data scientists, ML engineers, and domain experts to translate research prototypes into production‑ready tools;
• Build intuitive user interfaces and robust backend systems for data visualization, model interaction, and experimentation;
• Develop APIs and microservices to integrate with internal platforms and external data sources;
• Optimize performance, scalability, and reliability of R&D tools and applications;
• Participate in architectural discussions and contribute to technical strategy for R&D software infrastructure;
• Rapidly prototype new ideas and iterate based on feedback from researchers and stakeholders.

Bayer seeks an incumbent who possesses the following:

• Minimum of a Master’s Degree in Statistics, Mathematics, Informatics, Computer Sciences, ML Ops, Computer Engineering, or similar education;
• Proficiency in frontend technologies
  : such as React, Angular, or Vue.js, and AI enablement solutions such as Cursor;
• Strong experience with backend frameworks
  : like Node.js, Python (Flask, FastAPI, Django), or Java;
• Solid understanding of RESTful APIs
  , GraphQL
  , and Web Sockets
  ;
• Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes);
• Familiarity with CI/CD pipelines
  , Git, and agile development practices;
• Knowledge of database systems
  : such as SQL (PostgreSQL, MySQL) and No
  
  SQL (MongoDB, Redis);
• Ability to work in a collaborative, interdisciplinary team environment;
• Experienced in GxP/CSV & IT project and/or business process design;
• Extensive experience in Data Science capability assessment and analysis;
• Demonstrated ability to address and resolve conflicts; proven collaboration skills;
• Pharma and life sciences domain experience;
• Applicable security and compliance experience;
• Exposure to machine learning workflows or experience integrating ML models into applications;
• Experience with data visualization libraries (D3.js, Plotly, etc.);
• Understanding of MLOps or model deployment practices;
• Background in scientific computing, data…