Senior Scientist, Scientific Computing

Principal Responsibilities

• Exhibits effective software expertise through applied experience, grounded in scientific and business principles with a proven record of effectively supporting strategic efforts.
• Demonstrates competence in utilizing computational tools to analyze and visualize data for research and business objectives related to studies, experiments, trials, and processes. This includes full proficiency in relevant computational and scripting tools, including artificial intelligence (AI).
• This role goes beyond model prototyping. You will own the AI/ML layer end to end, from architecture and evaluation to the cloud infrastructure it runs on. You’ll work closely with statisticians and programmers, so comfort with R/Python for statistical work and data visualization is important, not optional.
• Design and build RAG pipelines, agent workflows, and prompt/context strategies across our AI initiatives.
• Set up and maintain repositories, CI/CD pipelines, and development environments for the wider SDS team.
• Supports Scientific Software Packages, High Performance Computing (HPC) Hardware and platforms, communication tools, and standards and good practices documentation. Also supports business system and process improvement areas.
• Identifies and implements innovative tools and solutions in process automation and scientific computing domains. Identifies and implements advanced techniques to address complex project or program features. Proven ability in problem-solving and troubleshooting, including supporting strategic scenario planning, quantitative decision-making, and business support.
• Collaborates cross-functionally to identify and resolve issues. Successfully addresses business needs through enhanced insights, productivity, and efficiency. Advises management of opportunities to increase compatibility, interoperability, stability and usability of solution architecture.
• Demonstrates strong skills in building relationships across functions. Is accountable as a point of contact for a specific project or effort.
• Collaborates effectively with SDS colleagues and external partners to meet program needs. Works cross-functionally to identify and resolve issues.
• Shares knowledge within and across functions. Represents Statistics & Decision Sciences in cross-functional and intra-departmental teams or working groups pertaining to scientific or process initiatives. Builds cross-functional partnerships with internal colleagues and external partners (e.g., contractors, consultants) to achieve results for project-specific needs, focusing on enhancing communication, efficiency, and productivity.
• Supervise contractors/special assignment personnel/interns/co-ops as required.
• Exhibits strong written, oral, and interpersonal communication skills, and demonstrates proficiency in collaborating with by translating intricate technical concepts for drug research and development partners. Effectively communicates findings to program stakeholders and broader non-technical audiences with visuals and storytelling.
• Takes responsibility and accountability for the quality and timeliness of project deliverables as assessed by stakeholders from respective projects or programs. Follows best practices for Software Engineering, Project Management, and Data Integrity. Aligns with partners following best practices for Good Clinical, Manufacturing, Laboratory, or other pertinent GxP activities and requirements. Complies with organization and company standard operating procedures in a timely fashion, such as required training and project time reporting. Ensures that documents, programs, and all other deliverables are consistent and adhere to project and company standards.
• Stand up and manage MCP servers connecting AI tools to internal data sources and applications.

Qualifications:

• PhD in Computer Science, Data Science, Statistics, Computational Biology, Engineering or a related quantitative field or Master’s with 3 to 5 years of software/ML engineering experience, including 1 to 2+ years working hands-on with LLMs in production.

Technical Skills:

• Strong Python; experience building production services, not just notebooks
• Working proficiency in R programming (or strong willingness/ability to read and work with R)
• Hands-on experience with LLM APIs (Anthropic, OpenAI, or similar) and at least one agent framework (e.g. LangGraph, LlamaIndex, or a custom framework)
• Databases
• Understanding of LLM failure modes (hallucination, context drift, prompt injection) and mitigation strategies.
• Hands-on AWS (S3, EC2, GPU instances) or Azure equivalent, including basic IAM/security and cost management
• Git-based workflows, CI/CD, containerization (Docker)
• Experience setting up MCP servers or comparable integration/orchestration tooling.

Required Skills:

Preferred Skills:

• Advanced Analytics
• Biostatistics
• Clinical Trials
• Collaboration
• Consulting
• Critical Thinking
• Data Privacy Standards
• Data Quality
• Data Savvy
• Digital Fluency
• Good Clinical Practice (GCP)
• Quality Assurance (QA)
• Report Writing
• Standard Operating Procedure (SOP)
• Statistical Analysis Systems (SAS) Programming
• Statistics
• Systems Development
• Technologically Savvy