Sr. Principal Scientist, Bioinformatics, Computational Biology, Precision Medicine Full-time

Bioinformatics Team Lead

This role will lead a Hyderabad-based team that delivers production-grade, reproducible workflows across multi-omics and related data types, and will help modernize the bioinformatics stack for the AI era, including responsible adoption of deep learning, foundation models, and agentic workflow patterns where appropriate. The ideal candidate combines deep bioinformatics, AI/ML and software engineering expertise with strong scientific judgment and a track record of leading teams in regulated or quality-driven environments. They will partner closely with computational biologists, biomarker scientists, translational researchers, data engineers, and platform leaders across global time zones to translate complex biological data into robust, scalable, and auditable data products and analyses.

Technical and architectural leadership

• Own the technical direction and architecture for production bioinformatics workflows and platforms spanning genomics, transcriptomics, single-cell, spatial omics, proteomics, epigenomics, metabolomics, imaging-derived features, and related biomarker modalities.
• Establish standards for pipeline modularity, reusability, and performance (e.g., reference data management, parameterization, caching, provenance, and resource optimization).
• Evaluate, select, and operationalize scalable compute patterns across HPC and cloud environments, including workflow orchestration (e.g., Nextflow/Snakemake) and modern data/compute platforms (e.g., Databricks) as appropriate.

Reproducibility, validation, data standards and quality

• Define and enforce best practices for reproducible bioinformatics delivery: version control (Git), containerization (Docker/Singularity), infrastructure-as-code where applicable, automated testing, CI/CD, and release management.
• Design and implement algorithm benchmarking and validation frameworks, including gold standards, reference datasets, acceptance criteria, and performance thresholds for clinical development use cases.
• Develop requirements for robust QC and data integrity controls across the end-to-end lifecycle (ingestion, processing, analysis-ready outputs), including audit trails and traceability suitable for regulated documentation. Requirements to feed into data ingestion and engineering activities.

Assay onboarding, algorithms, and vendor management

• Partner with internal biomarker laboratories and external CROs/vendors to onboard new assays and modalities; author and maintain data transfer specifications, interface control documents, metadata requirements, and acceptance criteria.
• Develop and document algorithms for the reporting of outputs from assays – characterize dynamic ranges, limits of quantitation, sensitivity and specificity metrics and technical variability.
• Develop algorithms for normalization in a manner fit for purpose for the experiment design.
• Ensure consistent application of data formats and standards (e.g., FASTQ, BAM/CRAM, VCF, MAF, HDF5, AnnData, Seurat objects) and promote strong metadata and sample lineage practices across programs.
• Drive root cause analysis and continuous improvement when quality issues arise (sample tracking, batch effects, assay drift, pipeline regressions, or vendor non-conformance).

AI and advanced analytics enablement (responsible by design)

• Collaborate with data science and platform partners to enable advanced ML/AI use cases on multi-modal omics and biomarker data, including deep learning, foundation model applications, and agentic workflow automation where they demonstrably improve quality, speed, or scalability.
• Define fit-for-purpose evaluation and monitoring approaches for AI-enabled components (e.g., leakage prevention, bias and robustness checks, calibration, drift monitoring, and human-in-the-loop review).
• Ensure that AI adoption is aligned with data privacy, security, and documentation expectations, and that solutions are productionized with measurable outcomes rather than prototype-only deliverables.
• Evolve system to agentic bioinformatics frameworks.

People leadership and team development

• Build, manage, and mentor a high-performing team of bioinformaticians and bioinformatics engineers; set clear expectations, provide technical coaching, and drive professional development and career growth.
• Implement strong engineering and scientific review practices (design reviews, code reviews, analysis reviews, post-incident reviews) to sustain quality and accelerate learning.
• Plan capacity and delivery; establish an execution cadence that works across major time zones and balances program priorities with platform sustainability.

Cross-functional partnership and communication

• Partner continuously with US-based Platform and Data Ingestion leads to ensure aligned priorities, seamless handoffs, and timely delivery of analysis-ready data products and validated pipelines.
• Translate complex methods and results for diverse audiences; produce clear technical documentation, methods summaries, and decision-ready materials, and represent bioinformatics in cross-functional and external forums as appropriate.
• Stay informed and abreast of latest developments in the field.

Required qualifications

Education & experience

• Master's or PhD in Bioinformatics, Computational Biology, Genetics/Genomics, Computer Science, Statistics, or related quantitative discipline.
• 13+ years of relevant experience delivering bioinformatics solutions in an industry setting (biopharma/biotech/healthcare or equivalent), including ownership of production pipelines and data products.
• 3+ years of people leadership and/or strong technical leadership with demonstrated experience hiring, mentoring, and leading teams or complex cross-functional initiatives.

Technical skills

• Expert programming skills in Python and R. Familiarity with lower-level languages (C/C++) a plus. Strong proficiency in R for statistical analysis; working knowledge of software engineering best practices (clean APIs, testing, packaging, dependency management).
• Deep understanding of biomarker assays and sequencing technologies (immunoassay, flow cytometry, immunohistochemistry, proteomics, whole genome sequencing, exome sequencing, targeted panel sequencing, bulk RNA-seq, methylation, metabolomics) and common analytical pitfalls (e.g., batch effects, confounding, sample swaps, assay drift, missingness, and multiple testing).
• Strong statistical reasoning and experience applying appropriate models to biological data.
• Hands-on experience with core omics workflows (alignment, variant calling, expression quantification, single-cell processing, fusion detection, copy number, structural variants, and downstream statistical methods).
• Experience with single cell RNA-seq and spatial transcriptomics data is required.
• Experience with workflow engines and reproducible pipelines (Nextflow, Snakemake, Airflow).
• Familiarity with containerization (Docker/Singularity), CI/CD, and version control (Git).
• Experience with cloud and HPC environments and scalable compute frameworks.
• Strong knowledge of data formats and standards (FASTQ, BAM/CRAM, VCF, MAF, HDF5, AnnData, seurat) and metadata best practices.
• Experience with benchmarking and validation frameworks, unit/integration testing for pipelines.
• Experience leading cross-functional technical projects and mentoring bioinformaticians. Excellent stakeholder management and communication skills.
• Prior experience in a biomedical/pharmaceutical environment
• Strong command of data and metadata standards, and the ability to implement robust validation and QC gates for analysis readiness.
• Excellent stakeholder management and communication skills, including the ability to write clear technical documentation and communicate across global teams in English.
• Familiarity with agentic frameworks and ability to transform traditional bioinformatics workflows into agentic infrastructure.