Lead the development and implementation of AI-driven evidence strategies for Lung, Head & Neck therapeutic areas, translating data into actionable insights for portfolio decisions.
Compensation
The annual base pay for this position ranges from $186,014.40 - 279,021.60 Annual USD.
Responsibilities
Define and evolve the Clinical Intelligence & Evidence strategy for Lung, Head & Neck
Lead end‑to‑end research, training, and deployment of recommender systems and GenAI models for Unity’s large‑scale mobile platform.
Compensation
This range reflects the anticipated base salary for this position. Beyond base salary, this role may be eligible for equity awards and participation in our company incentive plans (such as annual discretionary bonuses or sales commissions). The final offer amount will depend on several factors, including geographic location and the candidate’s relevant experience, professional background, and skill set.
Responsibilities
Research, train, and deploy state‑of‑the‑art recommender systems using deep learning and GenAI
Own projects end‑to‑end from business idea through modeling, experimentation, and production deployment
Build online, scalable models and serving solutions that leverage massive datasets
Apply multimodal and sequence‑modeling techniques to real product problems
Lead cross‑team initiatives, improve tooling, and mentor others
Lead evidence generation and analytics for oncology and hematology, translating complex data into decision-ready insights to influence Phase 3 investments, trial design, and patient access.
Compensation
The annual base pay for this position ranges from $144,648.80 - $184,000 USD. Base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition, our positions offer a short-term incentive bonus opportunity; eligibility to participate in our equity-based long-term incentive program.
Responsibilities
Prioritize evidence gaps with stakeholders and global product teams
Apply machine learning and causal inference to deliver robust answers on patient stratification, external control arm construction, prognostic risk adjustment, and treatment effect heterogeneity
Deliver and validate multimodal patient-level models integrating clinical, genomic, imaging, and real-world data
Turn internal and competitor trial data into clear insights on standard of care, patient pathways, benchmarking, and unmet need
Enable platform and tooling teams to bring new tools, agents, and experimental approaches into evidence generation
Requirements
PhD in a quantitative discipline such as epidemiology, biostatistics, computational biology, machine learning, health data science, or a related field
Nice to have: Domain expertise in hematology or oncology
Nice to have: Experience deploying analytics into clinical trial operations, submissions, or payer engagements
