Key Responsibilities and Required Skills for Geneticist

🎯 Role Definition

We are seeking a skilled Geneticist to design and execute genomics experiments, analyze high-throughput sequencing data, interpret genetic variants, and translate findings into actionable scientific or clinical insights. The Geneticist will operate at the intersection of wet lab molecular biology and computational genomics, taking ownership of experimental design, quality control, pipeline development, and stakeholder communication to drive research, diagnostic, or product development goals.

Key focus areas: next-generation sequencing (Illumina, PacBio, Oxford Nanopore), variant calling and annotation, population and clinical genetics (GWAS, linkage, pedigree analysis), CRISPR-based functional validation, reproducible bioinformatics workflows, and adherence to laboratory SOPs, quality systems, and regulatory requirements.

📈 Career Progression

Typical Career Path

Entry Point From:

Research Assistant / Laboratory Technician (molecular biology focus)
Bioinformatics Analyst / Genomics Data Analyst
Clinical Laboratory Scientist or Diagnostic Technologist

Advancement To:

Senior Scientist, Genetics / Senior Genomic Scientist
Lead Scientist, Genomics or R&D Manager
Principal Investigator / Director of Genetics or Head of Genomics

Lateral Moves:

Genomic Data Scientist / Computational Biologist
Clinical Geneticist / Diagnostic Variant Scientist

Core Responsibilities

Primary Functions

Design, plan, and execute genetic and genomic experiments including sample acquisition, DNA/RNA extraction, library preparation for short- and long-read sequencing, and targeted genotyping assays while ensuring sample integrity and chain-of-custody procedures.
Develop, validate and optimize next-generation sequencing (NGS) workflows for whole-genome, whole-exome, targeted panels, RNA-seq, and single-cell sequencing including library construction, enrichment strategies, and sequencing QC metrics.
Implement and maintain robust bioinformatics pipelines for read alignment (BWA, Bowtie2), variant calling (GATK, FreeBayes), structural variant detection, and annotation (VEP, ANNOVAR) to produce reproducible, well-documented results.
Perform variant interpretation and clinical/functional annotation using ACMG/AMP guidelines, population frequency resources (gnomAD), ClinVar, locus-specific databases, and literature review to support research findings or clinical reporting.
Conduct statistical genetics and population analyses such as GWAS, heritability estimation, PCA, admixture, and kinship analysis using PLINK, Hail, R, and specialized tools to identify genotype–phenotype associations.
Design and execute CRISPR/Cas9 experiments, gene-editing assays, and functional validation studies (knockout/knock-in) in cellular or model systems to assess variant pathogenicity and gene function.
Build, test, and deploy reproducible workflow management systems (Nextflow, Snakemake) and containerized environments (Docker, Singularity) to scale genomic analyses across compute clusters and cloud platforms (AWS, GCP).
Lead data quality assurance and quality control processes including sample QC, coverage and depth analysis, contamination detection, and implement corrective actions and assay re-runs as needed.
Collaborate with cross-functional teams (clinicians, statisticians, product managers, regulatory affairs) to translate genetic findings into clinical reports, diagnostic assays, therapeutic targets, or product features.
Produce clear, publication-quality figures, tables and written summaries of genetic results for peer-reviewed manuscripts, regulatory submissions, scientific presentations, and internal stakeholders.
Maintain and curate genomic datasets and metadata using LIMS and secure databases, ensuring accurate sample tracking, annotation, controlled access, and compliance with data privacy and consent requirements.
Mentor and train junior scientists, technicians, and students on experimental protocols, data analysis best practices, and interpretation of genetic results; contribute to building team capabilities.
Drive grant writing, protocol development, and experimental budgets by preparing study designs, power calculations, timelines, and resource plans for research funding or internal project approvals.
Ensure laboratory compliance with biosafety, ethical, and regulatory standards (IRB/HIPAA/GxP/CLIA where applicable), maintaining documentation, SOPs, and audit readiness.
Evaluate and integrate new technologies and assays (single-cell genomics, spatial transcriptomics, long-read sequencing, molecular barcoding) to extend organizational capabilities and maintain competitive advantage.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Maintain reagent, consumable inventory and procurement planning to ensure uninterrupted lab operations and track vendor performance and costs.
Troubleshoot technical issues across wet lab protocols and computational pipelines, implementing process improvements and corrective actions to reduce turnaround time and increase reproducibility.
Assist in regulatory submissions and preparation of documentation for diagnostic assays or research studies, including validation reports, method comparisons, and SOP updates.
Engage with external collaborators and core facilities to coordinate sample processing, data sharing agreements, and joint publications or product development.
Present findings at internal reviews, project meetings, scientific conferences, and customer engagements to communicate impact and next steps.
Contribute to patent filing, commercialization strategies, or translation of discoveries into diagnostic products or therapeutic programs.

Required Skills & Competencies

Hard Skills (Technical)

Expert knowledge of molecular biology techniques: DNA/RNA extraction, PCR/qPCR, Sanger sequencing, library preparation for Illumina/PacBio/ONT, and targeted enrichment methods.
Hands-on experience with NGS data processing: read alignment, variant calling (GATK, FreeBayes), structural variant detection, and variant annotation tools (VEP, ANNOVAR).
Proficiency in bioinformatics programming and data analysis: Python, R (Bioconductor), command-line Unix/Linux, and statistical modeling.
Experience with population and statistical genetics tools: PLINK, Hail, KING, ADMIXTURE, and GWAS pipelines.
Workflow automation and reproducibility: Nextflow, Snakemake, Git, Docker/Singularity, continuous integration for pipelines.
Familiarity with cloud computing and large-scale genomics infrastructure: AWS, GCP, HPC clusters, and cost-effective data storage/transfer strategies.
Knowledge of clinical genetics standards and variant interpretation frameworks (ACMG/AMP), ClinVar submission processes, and diagnostic reporting.
Experience with laboratory information management systems (LIMS), sample tracking, metadata curation, and secure data handling.
Competence in designing and analyzing CRISPR/Cas9 experiments, functional genomics assays, and cell-based model systems.
Ability to perform rigorous QC and validation studies, assay optimization, and creation of SOPs and validation documentation.

Soft Skills

Strong scientific communication skills: writing grants, manuscripts, clinical reports, and presenting technical results to non-technical stakeholders.
Critical thinking and problem-solving with meticulous attention to detail and data integrity.
Project management and prioritization, including coordinating multi-site studies and delivering on timelines.
Collaborative team player with experience working in cross-disciplinary teams (wet lab, bioinformatics, clinical, regulatory).
Mentoring and leadership ability to develop junior staff and build effective laboratory or computational teams.
Adaptability and continuous learning mindset to evaluate and adopt emerging genomics technologies and methods.
Ethical judgment and professionalism in handling sensitive genetic and patient data.

Education & Experience

Educational Background

Minimum Education:

Master’s degree (MS/MSc) in Genetics, Genomics, Molecular Biology, Bioinformatics, Computational Biology, or related life sciences field.

Preferred Education:

PhD in Genetics, Genomics, Human Genetics, Computational Biology, or a closely related discipline for senior or research-intensive roles.

Relevant Fields of Study:

Human Genetics, Molecular Biology, Genomics
Bioinformatics, Computational Biology, Biostatistics
Biotechnology, Biomedical Sciences, Genetic Epidemiology

Experience Requirements

Typical Experience Range:

2–5 years for mid-level Geneticist roles; 5–10+ years for senior roles or specialized clinical positions.

Preferred:

Experience in clinical or diagnostic laboratory settings (CLIA/CAP) is highly valued for clinical genetics roles.
Track record of publications, validated assays, successful grant applications, or demonstrated product development/commercialization experience.