Assoc. Scientist, Post Doc Fellow- Data Science… at Merck · USA -…

Summary: Merck is a research-driven biopharmaceutical company committed to delivering impactful medicines and vaccines. They are seeking a highly motivated Postdoctoral Research Fellow to develop and apply novel AI/ML methods for linking biofluid biomarkers with multi-omics data to drive precision patient subtyping and predictive modeling in neurodegenerative diseases, particularly Alzheimer's disease. 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 Required Qualifications: - PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Computational Biology/Bioinformatics, or a related quantitative field; or equivalent research experience - Demonstrated experience developing and applying new AI/ML models for healthcare/biomedical data (e.g., multi-omics, clinical/lab data, real-world data, imaging) - Strong grounding in probability, linear algebra, optimization, and numerical methods with a deep understanding of modern ML/DL algorithms - 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 - 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 - 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 Qualifications: - Knowledge of Alzheimer's disease or neurodegenerative biology - Experience with biomarker discovery and/or patient stratification - Expertise in one or more of multi-omics integration at scale (genomics, transcriptomics, proteomics; single-cell/spatial preferred), graph/network methods, causal inference Required Skills: AI/ML methods, Python, Deep learning frameworks, Multi-omics integration Important Skills: Computational Biology, Data Analysis, Statistical Analysis Nice-to-Have Skills: Scientific Research, Scientific Writing, Accountability, Adaptability Benefits: VISA Sponsorship: Yes, Medical, Dental, Vision healthcare and other insurance benefits (for employee and family), Retirement benefits, including 401(k), Paid holidays, Vacation, Compassionate and sick days