Expert II / Senior Expert I, Data Science, New Targets
About The Position
The Oncology Data Science team in Biomedical Research works at the intersection of oncology drug discovery, computational biology, AI/ML, and data engineering to advance innovative therapeutics across drug modalities. We are seeking a highly motivated Senior Expert I / Expert II, Data Science to join the New Targets team as an experienced individual contributor. This role will apply advanced statistical, machine learning, and computational approaches to generate biological insight from complex multi-modal datasets and support target discovery and prioritization. The successful candidate will bring expertise across multiple omics modalities and data types, including spatial transcriptomics, proteomics, digital pathology, mIF or similar high-plex tissue imaging modalities as well as other high-dimensional molecular data such as bulk and single-cell RNA-seq, WES, WGS, etc. The role requires both scientific depth and technical fluency, including modern AI-enabled workflows that improve productivity, reproducibility, and scale.
Requirements
- PhD (or equivalent experience) in Computational Biology, Bioinformatics, Statistics, Computer Science, or a related quantitative discipline
- Demonstrated expertise in the analysis of complex omics datasets and at least one of the following: spatial transcriptomics, proteomics, or digital pathology
- Strong programming skills in R and/or Python
- Experience working with high-performance computing (HPC) environments and workflow management systems
- Deep understanding of statistical modeling, machine learning, and data integration methodologies
- Familiarity with AI-assisted coding tools and modern data science productivity platforms
- Strong collaboration and communication skills, with the ability to work effectively in multidisciplinary teams
Nice To Haves
- Artificial Intelligence (AI)
- Biostatistics
- Change Management
- Curious Mindset
- Data Governance
- Data Literacy
- Data Quality
- Data Science
- Data Visualization
- Deep Learning
- Graph Algorithms
- Learning Agility
- Logistic Regression Model
- Machine Learning (ML)
- Machine Learning Algorithms
- Nlp (Neuro-Linguistic Programming) And Genai
- Pandas (Python)
- Python (Programming Language)
- R (Programming Language)
- Sql (Structured Query Language)
- Stakeholder Engagement
- Statistical Analysis
- Time Series Analysis
Responsibilities
- Lead the analysis and integration of multi-modal omics datasets, including spatial transcriptomics, proteomics, high-plex imaging, and sequencing-based platforms (e.g., RNA-seq, scRNA-seq, WES, WGS)
- Develop and apply advanced statistical, machine learning, and AI-driven methods to extract actionable biological insights from complex datasets
- Partner with cross-functional teams to design and execute studies supporting target identification, validation, and translational research
- Build and contribute to scalable, reproducible data science workflows and computational pipelines
- Ensure high standards of data quality, reproducibility, and scientific rigor in all analyses
- Evaluate and implement emerging technologies, including AI-assisted coding platforms and agentic tools, to enhance team effectiveness and innovation
- Communicate scientific findings clearly to both technical and non-technical stakeholders
- Contribute to knowledge sharing, best practices, and continuous improvement within the computational biology and data science community
Benefits
- health, life and disability benefits
- a 401(k) with company contribution and match
- a variety of other benefits
- a generous time off package including vacation, personal days, holidays and other leaves
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
