Assoc. Scientist, Post Doc Fellow- Data Science for Multi-omics and Biomarker Modeling in Neuroscience

Role Summary

Assoc. Scientist, Post Doc Fellow - Data Science for Multi-omics and Biomarker Modeling in Neuroscience. Join the Neuroscience Translational Analytics team within Data, AI & Genome Sciences at our Cambridge site. The role will develop and apply AI/ML methods linking biofluid biomarkers with tissue-based multi-omics to drive precision patient subtyping and predictive modeling in neurodegenerative diseases, with a primary focus on Alzheimer's disease. You will design end-to-end ML pipelines and new algorithms for cross-cohort data ingestion, harmonization, modeling, validation, and delivery of tools for in silico target perturbation and biomarker dynamics prediction—centered on AD biology while ensuring methods generalize to other disease and therapeutic areas. You will work closely with data scientists, computational biologists, and neuroscientists to deliver benchmarked models and reusable codebases that enable patient subtype specific mechanism discovery.

Responsibilities

• Design and implement end-to-end AI/ML pipelines for biomarker discovery and patient subgroup stratification using multi-modal data (e.g., multi-omics, clinical/lab values, imaging), including robust ETL (ingestion, QC, normalization, harmonization) and reproducible workflow orchestration.
• Develop and innovate on ML/DL methods tailored to study objectives: formalize hypotheses, translate mathematical ideas into implementable algorithms, conduct ablation and robustness analyses, and iterate based on emerging data and stakeholder feedback.
• Build and benchmark models beyond standard off-the-shelf approaches, including: multi-omics integration models at scale; models for patient subtyping and progression prediction models for in silico perturbation and biomarker dynamics.
• Translate model outputs into actionable insights: produce clear visualizations, summaries; deliver well-documented code, APIs, and reproducible analyses.
• Develop high-quality documentation and internal tools to enable reuse and scaling.
• Collaborate with computational, experimental, and clinical partners on study design, cohort definition, data interpretation, and prioritization of follow-up validation experiments.
• Contribute to scientific communication: publications and preprints, present findings to cross-functional teams, internal and external meetings and conferences.

Qualifications

• Required: PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Computational Biology/Bioinformatics, or a related quantitative field; or equivalent research experience.
• Required: Demonstrated experience developing and applying new AI/ML models for healthcare/biomedical data (e.g., multi-omics, clinical/lab data, real-world data, imaging).
• Required: Strong grounding in probability, linear algebra, optimization, and numerical methods with a deep understanding of modern ML/DL algorithms.
• Required: Ability to read, critique, and implement state-of-the-art ML papers, and to adapt/extend existing algorithms to novel biological problems and data types.
• Required: Proficiency in Python with solid object-oriented design and software engineering best practices; hands-on experience with deep learning frameworks (e.g., PyTorch) and end-to-end ML workflows; computer science foundations enabling translation from math to production code.
• Required: Strong scientific track record, demonstrated by peer-reviewed publications in AI/ML, computational biology, or related venues, or other scientific achievements (e.g., awards, patents, open-source contributions, grants).
• Preferred: Knowledge of Alzheimer’s disease or neurodegenerative biology.
• Preferred: Experience with biomarker discovery and/or patient stratification.
• Preferred: Expertise in one or more of multi-omics integration at scale (genomics, transcriptomics, proteomics; single-cell/spatial preferred), graph/network methods, causal inference.

Skills

• Accountability
• Adaptability
• Analytical Chemistry
• Cell Culture Techniques
• Computational Biology
• Data Analysis
• Immunoassays
• In Vivo Mouse Models
• Mathematical Biology
• Molecular Biology
• Parasitology
• Programming Languages
• Scientific Research
• Scientific Writing
• siRNA Knockdown
• Statistical Analysis
• Synthetic Chemistry

Additional Requirements

• Travel: 10%