Job Title: Data Scientist Spatial Transcriptomics

Location: Remote PAN India (Hyderabad preferred)

Employment Type: Contract 12 Months

Notice Period-Immediate-30 days

Working Hours: First Shift IST; occasional evening meetings with US teams between 7:00 PM and 10:00 PM IST may be required, generally around once per week.

Years of experience needed-8+ years

Role Overview

We are looking for a PhD-level Data Scientist / Computational Biologist with hands-on experience in spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) data analysis.

The role involves processing, analyzing, and interpreting complex biological datasets generated through experimental research. The ideal candidate will have a strong scientific understanding of how biological samples and sequencing data are generated, along with the computational expertise to independently perform advanced omics analysis and derive meaningful biological insights.

Candidates whose PhD dissertation or research involved practical spatial transcriptomics analysis are strongly preferred.

Fresh

PhD graduates with relevant hands-on research experience are welcome to apply.

Key Responsibilities

- Perform quality control, filtering, doublet detection, and contamination assessment of single-cell and spatial transcriptomics datasets.
- Conduct data normalization, batch correction, and dataset integration.
- Perform dimensionality reduction using PCA, UMAP, and t-SNE, along with clustering analyses.
- Carry out cell-type annotation using reference-based and marker-based approaches.
- Perform differential gene expression, pathway enrichment, and gene set analyses.
- Conduct trajectory inference, pseudotime analysis, and gene regulatory network analysis.
- Analyze spatial transcriptomics data, including spatial clustering, tissue domain identification,



cell-type mapping, spatial deconvolution, and cellular neighborhood analysis.
- Integrate and analyze multimodal datasets, including scATAC-seq and Multiome data, where applicable.
- Develop, maintain, and document reproducible computational pipelines and analytical workflows.
- Generate scientific reports, data visualizations, biological interpretations, and presentations for research stakeholders.
- Collaborate with experimental scientists and computational teams to translate biological questions into analytical approaches.

Required Qualifications

- PhD in Bioinformatics, Computational Biology, Genomics, Systems Biology, Biostatistics, Computer Science, or a related scientific discipline.
- Hands-on experience in spatial transcriptomics analysis, preferably acquired during PhD research, dissertation work, or scientific projects.
- Strong experience in computational analysis and processing of biological sample data.
- Advanced proficiency in R and Python.
- Experience with bioinformatics tools and frameworks such as Seurat, Scanpy, and Bioconductor.
- Strong understanding of statistics, machine learning, and computational methods for high-dimensional omics data.
- Experience with Linux/Unix environments and, preferably, AWS cloud platforms and high-performance computing (HPC).
- Ability to independently execute analytical projects,



from data processing and quality control through biological interpretation.
- Strong scientific communication skills and the ability to collaborate with laboratory and computational research teams.

Preferred Skills

- Experience with 10x Genomics technologies, particularly Visium.
- Strong background in single-cell RNA sequencing (scRNA-seq) and single-cell data analysis.
- Experience in transcriptomics, genomics, computational biology, or bioinformatics research.
- Familiarity with human, medical, animal, or other biological tissue datasets.
- Experience working directly with experimental or laboratory scientists.
- Experience with spatial deconvolution, cell-type mapping, multimodal omics integration, and spatial analysis workflows.

Ideal Candidate Profile
- PhD-level computational biologist, bioinformatician, or genomics researcher.
- Demonstrated hands-on experience with spatial transcriptomics during doctoral or research work.
- Ability to independently process and analyze sequencing data and interpret biological results.
- Understanding of the experimental methods used to generate the underlying biological data.
- Strong analytical, problem-solving, and scientific communication skills.

Significant: Candidates with relevant PhD research and practical spatial transcriptomics experience will be prioritized. General software development or programming experience without relevant biological data analysis is not sufficient.

How to Apply

Interested candidates can share their updated CV highlighting their PhD research, spatial transcriptomics projects, sequencing technologies, analytical tools, and relevant publications or research outcomes.

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📌 Data Scientist – Spatial Transcriptomics (India)
🏢 HYR Global Source
📍 India