Biological Data Scientist

Company Overview 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.

Position Overview
We are seeking a talented Biological Data Scientist to join our team of researchers. The ideal candidate will have a strong foundation in biological systems modeling combined with expertise in computational methods and data science. This role emphasizes mathematical modeling of biological networks and processes. You will help translate biological complexity into actionable insights that drive our drug development platform.

Key Responsibilities
Technical Development:

• Develop and implement mathematical models of biological systems, including gene regulatory networks and signaling pathways
• Design computational simulations of biological processes to enhance our understanding of drug mechanisms and treatment responses.
• Integrate biological knowledge with machine learning approaches to improve our causal inference methods
• Collaborate with senior scientists to develop biologically-informed features for our analytical platforms

Research & Analysis:

• Apply systems biology approaches to analyze clinical trial data and identify responder patterns
• Develop methods that incorporate biological mechanisms into treatment effect estimation
• Design and validate quantitative models that predict drug responses based on biological principles
• Document and present biological models and results to both technical and non-technical stakeholders

Collaboration:

• Work cross-functionally with clinical experts, data scientists, and engineering teams
• Contribute to technical discussions by providing biological context and insights
• Support the preparation of technical documentation and research papers
• Help bridge the gap between biological understanding and computational methodsQualifications
  Master\'s or PhD in Systems Biology, Computational Biology, Biophysics, Mathematical Biology, or related field
  Strong foundation in both biological sciences and quantitative methodsExperience:
  3+ years of experience in biological modeling or systems biology
  Experience applying mathematical models to biological questions
  Background in both wet lab biology and computational analysis is highly valuedSkills:
  Strong programming skills in Python (R in addition – an advantage)
  Proficiency in mathematical modeling techniques (differential equations, dynamical systems, Bayesian Networks)
  Experience with network analysis and biological circuit modeling
  Strong analytical and problem-solving skills
  Ability to translate complex biological concepts into computational frameworks

  Preferred:
  Familiarity with causal inference concepts
  Publication record in systems biology or related fields
  Experience in pharmaceutical or biotech industry
  Background in synthetic biology or systems design

  What We Offer:
  Join us on a mission to bring more effective drugs to more patients
  The opportunity to work at the intersection of cutting-edge biology and advanced data science
  Extremely hard and diverse problem setting, in which the team is pushing the envelope
  A collaborative and dynamic startup environment
  Professional growth and development opportunities