About the Position
As a Senior Data Scientist - Medical Imaging at Formation Bio, you will lead our efforts to integrate advanced imaging analytics and AI into our drug development pipeline. In this role, you'll pioneer the application of computer vision and deep learning to medical imaging data (MRI, X-ray, CT, PET) to accelerate clinical trials and improve patient outcomes. You'll develop state-of-the-art models for automated image analysis, disease progression monitoring, and treatment response assessment. Working closely with clinical teams, radiologists, and drug development experts, you'll translate complex imaging data into actionable insights that drive key decisions across our portfolio. This position offers a unique opportunity to revolutionize how medical imaging is leveraged in pharmaceutical development while directly impacting patient care.
Responsibilities
Lead the development of advanced deep learning models for medical image analysis, focusing on MRI and X-ray modalities for clinical trial applications
Design and implement automated image processing pipelines for disease detection, segmentation, and quantification across multiple therapeutic areas
Create innovative approaches for longitudinal image analysis to track disease progression and treatment efficacy in clinical trials, incl. AI-based biomarkers and surrogate endpoints
Collaborate with clinical operations to integrate imaging AI solutions into trial protocols and workflows
Partner with regulatory teams to ensure imaging biomarkers and AI models meet FDA standards for clinical trial endpoints
Build scalable infrastructure for processing and analyzing large-scale medical imaging datasets from multi-site clinical trials
Present imaging insights and model performance to executive leadership and external partners
About You
Required Qualifications
PhD in Computer Science, Biomedical Engineering, Medical Physics, or related field with focus on medical imaging
5+ years of post-PhD industry experience developing production-grade AI models for medical imaging applications
Deep expertise in computer vision and deep learning architectures (CNNs, U-Net, Vision Transformers) for medical image analysis
Proven track record with MRI and X-ray image analysis, including hands-on experience with DICOM standards and medical imaging software
Strong programming skills in Python with expertise in PyTorch/TensorFlow and medical imaging libraries
Demonstrated ability to collaborate with radiologists and clinical teams to translate medical insights into technical solutions
Strong communication skills with proven ability to present complex imaging analytics to diverse stakeholders
Preferred Qualifications
Experience with regulatory submissions involving imaging endpoints or AI-based image analysis tools
Experience with multi-modal imaging fusion and analysis (combining MRI, CT, PET data)
Knowledge of clinical trial design and experience with imaging core lab operations
Experience with federated learning or privacy-preserving techniques for medical imaging
Understanding of radiomics and quantitative imaging biomarker development
Experience with 3D image reconstruction and volumetric analysis
Total Compensation Range: $177,500 - $232,000
Design, develop, and deploy production computer vision systems that analyze complex movement in video, owning technical decisions and collaborating with client teams on a sports performance solution.
Leads high-impact AI projects in life sciences, defining data science approaches and software architecture while mentoring colleagues, shaping client engagements, and ensuring rigorous delivery.
Desenvolve modelos estatísticos e de machine learning de ponta a ponta, com foco inicial em risco de crédito, e cria soluções de analytics e BI em parceria com equipes de negócios e clientes.
Desenvolve e conduz projetos de ciência de dados de ponta a ponta, criando modelos estatísticos, preditivos e de recomendação para apoiar decisões acadêmicas e de negócio, com rigor, explicabilidade e impacto mensurável.
Own and improve matching, recommendation, and taxonomy mapping models, guiding their full lifecycle from evaluation and experimentation through implementation while partnering with product and engineering teams.
