Data Scientist Jobs

About The Position

Requirements

• Ph.D. in Bioinformatics, Computational Biology, Data Science, Epidemiology, or a related field (0–2 years post-graduate experience); or MS with 2–4 years of experience; or BS with 4+ years of relevant experience.
• Minimum 2 years of experience working with large-scale biological or population datasets, preferably including experience analyzing immune system aging/function within the context of human and /or mouse data.
• Strong proficiency in Python or R.
• Solid understanding of statistical analysis and foundational machine learning techniques.
• Hands-on experience with NGS data analysis (e.g., RNA-seq, DNA methylation, ChIP-seq, or ATAC-seq).
• Experience with, or a strong desire to learn, proteomic data analysis and multi-omic data integration.
• Excellent problem-solving skills, attention to detail, and the ability to manage multiple tasks in a fast-paced environment.
• Ability to clearly present data and technical workflows to a multidisciplinary team.

Nice To Haves

• Prior experience or familiarity with biomarkers of immune system aging/function.
• Prior experience or internship in the pharmaceutical or biotechnology industry.
• Prior experience running large-scale association testing (e.g., genome-wide association studies [GWAS], epigenome-wide association studies [EWAS], proteome-wide association studies).
• Familiarity with methods in statistical genetics (e.g., Mendelian randomization, fine mapping, colocalization).
• Familiarity with machine learning analysis architectures (e.g., random forest, gradient boosting, transformers).
• Familiarity with public biological databases (e.g., GTEx, TCGA), epidemiological cohort data (e.g., TOPMed cohorts), or biobanks (e.g., UK Biobank, FinnGen).
• Ability to apply integrated generative protein design pipelines - from target-conditioned backbone generation through sequence design to computational fold validation - to support the development of novel therapeutic biologics with optimized specificity and developability properties.
• Working knowledge of computational histology pipelines incorporating modern deep learning approaches - including self-supervised and weakly supervised learning (MIL, DINO) and histopathology foundation models (e.g. UNI, CONCH) - to enable scalable, label-efficient classification of complex tissue phenotypes.
• Familiarity or prior experience with agentic AI in the context of analysis code pipeline development and biological analysis.
• Evidence of scientific contribution through publications, posters, or GitHub repositories.

Responsibilities

• Process and analyze large-scale biobank datasets, human population data, and in-vitro biological data using established analysis pipelines.
• Apply analytical methods and machine learning algorithms to help identify potential therapeutic targets and biomarkers.
• Partner with wet-lab scientists to analyze experimental results for target identification and Mechanism of Action (MOA) studies.
• Generate high-quality visualizations and reports to communicate findings to the project team.
• Provide high-quality data and computational insights that contribute to the development of biomarker strategies.
• Actively participate in team meetings, presenting data-driven insights to help the group meet project milestones.
• Stay current with the latest developments in data science and bioinformatics tools.

Benefits

• Competitive remuneration and benefits apply
• Competitive Total Reward program including a market driven base salary, bonus and long-term incentive.
• Generous paid time off program
• Comprehensive benefits package
• Eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles.
• Qualified retirement programs
• Paid time off (i.e., vacation, holiday, and leaves)
• Health, dental, and vision coverage in accordance with the terms of the applicable plans.