PhaseV provides an AI/ML clinical-development platform offering Bayesian/adaptive trial optimization, causal ML for heterogeneous treatment effects and disease modeling, plus operations tools for site selection, monitoring, virtual control arms.
<p> </p> <p><strong>Company Overview:</strong></p> <p>PhaseV (HQ in Boston, R&D in Tel Aviv) is a cutting-edge startup, pushing innovation in the drug development process to bring new treatments to more patients, in a more efficient and precise way. Leveraging the power of advanced causal inference and pushing the boundaries of machine learning, PhaseV detects hidden signals in clinical data and extracts actionable insights for planning the optimal trial. The company's proprietary ML-based platform for adaptive clinical trial design and closed-loop execution enables sponsors to easily and quickly unlock the potential of adaptive clinical trials, reveal the optimal adaptive design to meet their trial objectives and execute more efficient, more precise, and more successful clinical trials. Collaborating with 7 of the top pharmaceutical companies, as well as multiple CROs and biotechs, we are poised to make a significant impact on how drugs are developed and brought to market.</p> <p> </p> <p> </p> <p><strong>Position Overview:</strong></p> <p>We are seeking a talented Data Scientist to join our team of researchers. The ideal candidate will have a strong foundation in machine learning and statistics with an interest in causal inference. A couple of recent preprints we’ve released to get a sense of the challenges:</p> <ul> <li>Causal Responder Detection (https://arxiv.org/abs/2406.17571)</li> <li>Robust CATE Estimation Using Novel Ensemble Methods. (https://arxiv.org/abs/2407.03690)</li> </ul> <p><strong>Key Responsibilities:</strong></p> <p>Technical Development:</p> <ul> <li>Implement and optimize ML algorithms, with a focus on causal ML applications in clinical trials</li> <li>Collaborate with senior data scientists to develop and improve our analytical platforms</li> <li>Work alongside clinical and biological experts to translate complex problems into technical solutions</li> </ul> <p>Research & Analysis:</p> <ul> <li>Conduct rigorous statistical analyses of both randomized and observational data</li> <li>Develop and validate methods for responder identification and treatment effect estimation</li> <li>Document and present methodologies and results for internal and external stakeholders</li> </ul> <p>Collaboration:</p> <ul> <li>Work cross-functionally with engineering and product teams</li> <li>Contribute to technical discussions and peer code reviews</li> <li>Support the preparation of technical documentation and research papers</li> </ul> <p> </p> <p><strong>Qualifications:</strong></p> <p>Education:</p> <ul> <li>Master's in Computer Science, Statistics, Data Science, Engineering or a related field</li> <li>Ph.D. is an advantage but not required</li> </ul> <p>Experience:</p> <ul> <li>3+ years of experience in data science or machine learning roles</li> <li>Experience with ML in a practical research or industry setting</li> <li>Background in statistical modeling and analysis</li> </ul> <p>Skills:</p> <ul> <li>Strong programming skills in Python (R in addition - an advantage)</li> <li>Proficiency in statistical analysis and machine learning</li> <li>Experience with data visualization and communication of technical concepts</li> <li>Strong analytical and problem-solving skills</li> </ul> <p>Preferred:</p> <ul> <li>Familiarity with causal inference concepts</li> <li>Experience in healthcare or biotech industry</li> <li>Experience with large-scale data processing</li> </ul> <p> </p> <p><strong>What We Offer:</strong></p> <ul> <li>Join us on a mission to bring more effective drugs to more patients.</li> <li>The opportunity to work with an amazing team passionate about making a real-world impact.</li> <li>Extremely hard and diverse problem setting, in which the team is pushing the envelope.</li> <li>A collaborative and dynamic startup environment.</li> <li>Professional growth and development opportunities.</li> </ul> <p> </p>