Principal Scientist — AI-Powered Discovery for Neurodegeneration
Sanofi
June 27, 2026
Remote friendly (Cambridge, MA)
United States
$122,250 - $176,583.33 USD yearly
Clinical Research and Development
Main Responsibilities
- Lead machine learning/AI discovery efforts for Alzheimer’s, Parkinson’s, ALS/FTD, and Multiple Sclerosis.
- Drive end-to-end analyses, integration, and harmonization of multi-omics (human brain single-cell and spatial omics; CSF/plasma proteogenomic biomarkers; CRISPR functional screening) to create decision-grade resources.
- Integrate single-cell genomics with plasma/CSF proteomics and clinical outcomes to identify disease progression cell state shifts and molecular features, supporting patient selection for future trials.
- Champion “Everyday AI” and lead cross-functional upskilling to build AI literacy and digital fluency across the Neurology TA.
- Lead authorship of impactful scientific publications.
Education
- Bachelor’s in a quantitative/life science field, followed by a Ph.D. in Computational Biology/Bioinformatics/Neuroscience (or related).
Required/Basic Qualifications
- 4+ years post-PhD in biotech or academia; strong computational biology track record in neurodegeneration.
- Deep expertise in multi-omics, single-cell genomics, and spatial transcriptomics.
- Proven neurodegenerative disease research experience (AD, PD, MS, ALS/FTD).
- Experience analyzing human brain single-cell genomics and/or CSF/PBMC atlases; proteomic analysis from patient tissue/biofluids.
- Omics analysis knowledge in mouse/cellular neurodegeneration models.
- Expertise building workflows for cryptic splicing in scRNAseq, long-read, and spatial transcriptomics.
- AI/ML experience mapping gene regulatory landscapes of disease-associated cell states (single-cell RNA/ATAC and splicing datasets).
- Strong neuroscience, bioinformatics, pharmacology, life science, statistics foundations; excellent communication in multidisciplinary settings.
Preferred Qualifications
- Highly motivated collaborator; ability to translate computational work for non-computational stakeholders; strong problem-solving and independent work in fast-paced environments.
- Lead machine learning/AI discovery efforts for Alzheimer’s, Parkinson’s, ALS/FTD, and Multiple Sclerosis.
- Drive end-to-end analyses, integration, and harmonization of multi-omics (human brain single-cell and spatial omics; CSF/plasma proteogenomic biomarkers; CRISPR functional screening) to create decision-grade resources.
- Integrate single-cell genomics with plasma/CSF proteomics and clinical outcomes to identify disease progression cell state shifts and molecular features, supporting patient selection for future trials.
- Champion “Everyday AI” and lead cross-functional upskilling to build AI literacy and digital fluency across the Neurology TA.
- Lead authorship of impactful scientific publications.
Education
- Bachelor’s in a quantitative/life science field, followed by a Ph.D. in Computational Biology/Bioinformatics/Neuroscience (or related).
Required/Basic Qualifications
- 4+ years post-PhD in biotech or academia; strong computational biology track record in neurodegeneration.
- Deep expertise in multi-omics, single-cell genomics, and spatial transcriptomics.
- Proven neurodegenerative disease research experience (AD, PD, MS, ALS/FTD).
- Experience analyzing human brain single-cell genomics and/or CSF/PBMC atlases; proteomic analysis from patient tissue/biofluids.
- Omics analysis knowledge in mouse/cellular neurodegeneration models.
- Expertise building workflows for cryptic splicing in scRNAseq, long-read, and spatial transcriptomics.
- AI/ML experience mapping gene regulatory landscapes of disease-associated cell states (single-cell RNA/ATAC and splicing datasets).
- Strong neuroscience, bioinformatics, pharmacology, life science, statistics foundations; excellent communication in multidisciplinary settings.
Preferred Qualifications
- Highly motivated collaborator; ability to translate computational work for non-computational stakeholders; strong problem-solving and independent work in fast-paced environments.