Principal Scientist, Predictive Modeling & Applied AI

Role Description

Reimagine the infrastructure of cancer care within a community that values integrity, inspires growth, and is uniquely positioned to create a more modern, connected oncology ecosystem. We’re looking for a Principal Scientist in Predictive Modeling & Applied AI to help us accomplish our mission to improve and extend lives by learning from the experience of every person with cancer. Are you ready to be the next changemaker in cancer care?

In this role, you will operate as a scientific leader within the Research Sciences (RS) organization, supporting our Scientific Engagement and Applied Research (SEAR) function, innovating and translating predictive modeling approaches—such as digital twins and other advanced simulation frameworks into decision grade solutions for pharmaceutical and academic partners. Specifically, you will:

• Lead the design, development, and validation of advanced predictive modeling solutions, for digital twins and other patient-level simulation approaches, in clinical development and adjacent use cases (e.g., trial design, cohort selection, endpoint prediction, treatment effect estimation, etc.)
• Advance a methodological strategy against existing and future use cases for applied AI in RWD/RWE by appropriate application of machine-learning, deep learning, causal inference, and multimodal modeling approaches
• Ensure that prediction models are scientifically rigorous, clinically grounded, and aligned to decision-oriented use cases
• Serve as a scientific lead in client engagements, working closely with our Life Sciences Partnership team to apply, or proactively develop, Flatiron’s modeling strategies and solutions to biopharma clinical development needs
• Translate complex methodological concepts into clear, decision-relevant insights for technical and non-technical stakeholders through presentations, reports and other modes of engagement
• Act as the organization’s external scientific engagement lead for predictive modeling and applied AI, representing the company at national and international conferences, industry forums, and through publications and scientific communications
• Lead authorship of abstracts, manuscripts, and external publications, establishing Flatiron as a leader in applied AI and predictive modeling
• Develop and disseminate training on the application of predictive models and applied AI solutions to multiple cross-functional partners in a highly matrixed environment
• Partner with Product, Engineering, and Data teams to shape reusable capabilities into existing or novel scalable platforms for predictive analytics

Qualifications

• Advanced degree (MS, PhD, or equivalent experience) in a quantitative field (e.g., epidemiology, machine learning, biostatistics, data science, applied mathematics), or demonstrated equivalent expertise through applied work in predictive modeling in industry settings, including work with pharmaceutical or life sciences organizations, or academic/healthcare systems
• Fluent across a spectrum of predictive modeling approaches spanning gradient boosting (e.g., XGBoost), deep learning (e.g., neural networks for multimodal clinical data), and advanced statistical methods for longitudinal/time-to-event data
• Strong experience in developing machine learning or predictive modeling solutions for clinical research or clinical care applications such as digital twins, clinical trial simulations
• Strong familiarity with clinical development operations and processes, including clinical development planning, clinical trial design and analysis
• Strong experience in RWE methods in oncology, and familiar with variables and endpoints commonly used in oncology RWE research, observational studies and their intersection with randomized controlled trials
• Comfortable operating in a matrixed, fast-paced environment and balancing multiple high-priority initiatives
• Proficient in Python OR R programming
• Experience with large-scale, longitudinal healthcare datasets (e.g., EHR, claims, or multimodal data)

Requirements

• Experience applying predictive modeling to other drug development areas of practice including Early development, Commercial Analytics, and HEOR
• Track record of applying predictive modeling to inform key drug development decisions across the lifecycle (e.g., target selection, trial design, go/no-go decisions, launch readiness)
• Experience in clinical trial data management, analytics, and governance

Benefits

• Work/life autonomy via flexible work hours and flexible paid time off
• Comprehensive compensation package
• 401(k) contribution to help you reach your retirement planning goals
• Financial health resources including 1:1 financial advice
• Mental well-being tools and services
• Parental benefits and policies including family-building care and generous leave
• Path to parenthood programs supporting fertility, adoption and surrogacy
• Travel support for safe healthcare services