Hybrid – Cambridge

This is a short 3 months contract role

Looking for local candidates only

Python programming

• Currently working on Scales model, Sccraft model (Well established models)
• Working on data sets (single cell)
• Exp running single cell analyzers
• scanpy, panda, Matplotlib, Seaborn exp (Must have)
• Machine learning exp for integrating single cell
• Role is more toward bioinformatics and not towards AI/ML models
• Exp working with spatial data sets is an added advantage
• Some who has exp building a model from scratch will be a fit

Top 3 – 5 Skills Needed:

• Proficiency in programming with Python
• Solid understanding of data analysis and visualization techniques
• Experience working with single cell RNA-seq data
• Familiarity with basic machine learning concepts
• Domain knowledge of bioinformatics

Job Description:

• We are looking for a motivated and talented individual to join  as a Computational Scientist.
• The successful candidate will work closely with stakeholders in the group to integrate the internal single cell datasets from peripheral blood mononuclear cells treated with multiple experimental conditions into a combined atlas.
• The role will focus on applying AI/ML and advanced computational methods to integrate large-scale single-cell transcriptomic datasets to benchmark and prioritize treatments and assess which assets reverse disease specific signatures.

Key Responsibilities

• Build a Single-Cell Atlas: Utilize already generated single-cell datasets to build a comprehensive single-cell atlas.
• Analyze Single-Cell Data: Assist in the analysis of single-cell RNA-seq data to identify disease and treatment associated regulatory networks and biomarkers.
• Communicate Findings: Support the team in interpreting results and presenting scientific data to both internal and external stakeholders.
• Collaborate with Teams: Work with cross-functional teams to leverage computational methods in therapeutic development.

Impact

• By building the Atlas, the candidate will enable integrating single cell experiments generated via different experimental conditions into a combined atlas.
• This will facilitate comparison and integration of asset signatures across experiments, allowing systematic evaluation of which asset produces the most distinct, robust, or therapeutically relevant cellular responses under various stimulation and treatment paradigms.

Qualifications:

• MS degree (5+ years of experience) or PhD (0+ years of experience) in a quantitative field (Bioinformatics, Computational Biology, Computer Science, Computational Biology, or related a field).
• Some experience in a relevant academic or industry setting is preferred.
• Proficiency in programming languages, particularly Python, with a solid understanding of data analysis and visualization techniques.
• Familiarity with omics data types (e.g., RNA-seq) and basic machine learning concepts.
• Strong problem-solving skills and a desire to learn.

Preferred Technical Skills:

• Experience with Pandas, Scikit-learn, Matplotlib, Seaborn and Scanpy.
• Proficiency with Git for version control and collaboration.
• Hands-on experience with single-cell data analysis tools (e.g., Scanpy, Seurat, Bioconductor, or equivalent).
• Exposure to multi-modal integration methods (e.g., CITE-seq, ATAC-seq, proteomics, imaging mass cytometry, spatial transcriptomics).

Additional Technical Skills (a plus):

• Knowledge of cell type annotation, clustering, and trajectory inference methods.
• Experience building multi-modal AI/ML models that link transcriptomic, proteomic, and imaging data.