Senior Data Scientist (AI-assisted Clinical Development)

Location: Boston, Massachusetts, United States, or New York City, New York, United States. The position is based in an onsite setting. Relocation assistance is not available.

Salary: $142,700–$264,900 USD annually, based on the primary location of Boston, Massachusetts. A discretionary annual bonus may be available.

Company

hirify.global develops medicines and invests in research and development to advance healthcare and address serious and life-threatening diseases.

What you will do

• Apply advanced statistical and machine learning methods to build tools and software products for evidence generation, trial design, and decision support across research and development.
• Design simulation studies and comparative evaluations of analytical approaches for clinical and operational scenarios.
• Translate scientific problems into data science workflows and identify workflows that can be automated and productized.
• Build and maintain model development pipelines with traceability, performance monitoring, testing, and reproducibility.
• Integrate models and algorithms into production applications and contribute to compliant, interpretable machine learning product architectures.
• Document modeling decisions, trade-offs, and performance summaries while collaborating with product managers, software engineers, UX researchers, domain experts, and scientific and non-scientific stakeholders.

Requirements

• Master’s or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Bioinformatics, or a related field.
• 3+ years of experience applying machine learning and statistical modeling to data-centric problems in research or product environments.
• Proficiency with Python or R and machine learning libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience with experimental design, simulation, and model-performance evaluation.
• Ability to translate scientific or business questions into model architectures and data pipelines.
• Practical experience with version control, testing frameworks, and collaborative software development.
• Experience with one or more of RWD/RWE, decision analysis, clinical biomarkers, Bayesian inference, or interpretable machine learning.
• Ability to manage and prioritize multiple long-term and short-term projects, with strong attention to detail and quality.
• Excellent collaboration, statistical consulting, interpersonal, presentation, writing, problem-solving, critical-thinking, and decision-making skills, including the ability to influence without authority and explain complex technical concepts clearly.
• Ability to work effectively with scientific and non-scientific partners, think independently, demonstrate strategic agility, and respect cultural differences in a global workplace.

Nice to have

• Experience contributing to machine learning product pipelines and deployment workflows.
• Experience building evidence synthesis models, such as network meta-analysis, or constructing data-driven priors for drug development.
• Experience with Bayesian computing or probabilistic programming languages, including Stan, PyMC, or brms.
• Experience with collaborative coding practices and modern version control, including Git and CI/CD.
• Experience with generative AI tooling for software development, including agentic coding, spec-driven development, or AI code reviews.
• Experience developing agentic AI tooling for data analysis or processes relevant to the pharmaceutical industry.
• Knowledge of compliance-related standards such as GxP or HIPAA.
• Publication or public presentation experience in applied statistics or machine learning.
• Experience working in agile, cross-functional teams with scientific or product stakeholders.

Culture & Benefits

• Work within the Innovation Accelerator team, supporting design, data, and data science innovation strategy in Product Development Data Sciences.
• Collaborate with internal Roche teams and external partners to explore, prototype, and productize solutions.
• Contribute to internal knowledge sharing, peer reviews, and community learning within the data science function.
• Benefits are provided according to the company’s benefits program.
• A discretionary annual bonus may be available based on individual and company performance.