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PhaseV is a Boston-based startup (with R&D in Tel Aviv) developing an ML-powered platform for adaptive clinical trial design and execution. The company leverages advanced causal inference and machine learning to detect hidden signals in clinical data and extract actionable insights for optimizing trial design. PhaseV collaborates with seven top pharmaceutical companies, multiple CROs, and biotechs to make drug development more efficient and precise.
As a Senior Data Scientist, you will be an integral member of the research team, working on cutting-edge problems in causal inference applied to clinical trials. You will have direct client interaction, autonomy in your work, and the opportunity to make meaningful impact on a growing business.
Key Responsibilities:
Technical Development: Implement and optimize machine learning algorithms with a focus on causal ML applications in clinical trials. Collaborate with senior data scientists to develop and improve analytical platforms. Work alongside clinical and biological experts to translate complex problems into technical solutions.
Research & Analysis: Conduct rigorous statistical analyses of randomized and observational data. Develop and validate methods for responder identification and treatment effect estimation. Document and present methodologies and results for internal and external stakeholders.
Collaboration: Work cross-functionally with engineering, product, and business development teams. Contribute to technical discussions and peer code reviews. Support preparation of technical documentation and research papers.
The role offers strong communication requirements and the ability to navigate ambiguity with proactiveness. Recent company research includes work on Causal Responder Detection and Robust CATE Estimation using novel ensemble methods.
Qualifications:
Education: Master's degree in Computer Science, Statistics, Data Science, Engineering, or related field. Ph.D. is an advantage but not required.
Experience: 3+ years in data science or machine learning roles. Experience with ML in practical research or industry settings. Background in statistical modeling and analysis. Work experience in biotechnology or life sciences environment.
Skills: Strong programming in Python (R is an advantage). Proficiency in statistical analysis and machine learning. Experience with data visualization and communication of technical concepts. Strong analytical and problem-solving abilities. Ability to communicate results clearly and effectively.
Preferred: Familiarity with causal inference concepts. Experience with large-scale data processing.
