SeniorLead Health Data Scientist

Optima Partners is a leading professional services firm experiencing rapid growth and innovation. As part of our expansion we are dedicated to advancing our capabilities in data science and statistical genetics. We are seeking an exceptional Lead Data Scientist specialising in Statistical Genetics or bioinformatics to join our team. This role will be pivotal in driving our genetic research initiatives and contributing to cuttingedge solutions that enhance our services.

Key Responsibilities

Strategic Leadership (only for the Lead position)

• Lead the development and execution of data science strategies focused on statistical genetics bioinformatics and computational biology.
• Collaborate with crossfunctional teams to integrate genetic data insights into broader business strategies.
• Provide thought leadership in the application of statistical genetics to solve complex biological and medical problems.

Research Development

• Design and conduct advanced statistical analyses of largescale genetic and genomic datasets.
• Ability to interpret the results and find tangible link between the outcomes and the methodology applied.
• Develop and validate predictive models to understand genetic contributions to complex traits and diseases.
• Stay abreast of the latest advancements in statistical genetics and bioinformatics incorporating relevant techniques into ongoing projects.

Technical Expertise

Required

• Apply statistical genetics methodologies such as genomewide association studies (GWAS) metaanalysis polygenic risk scoring heritability.
• Apply genetics causal inference methodologies such as finemapping colocalization and Mendelian randomisation.
• Scientifically support ideation design development and maintenance of the largescale pipelines and workflows.
• Ensure robust data processing pipelines and workflows for handling largescale genomic data.
• Familiarity with target validation approaches such as ontology pathway enrichment gene and protein annotations diseasephenotype associations.

Preferred

• Develop statistically grounded methodologies to arrive to practical solutions.
• Data Management Compliance
• Ensure the integrity security and confidentiality of genetic data in compliance with relevant regulations.
• Implement and maintain high standards for data quality and reproducibility in research findings.

Communication Reporting

• Communicate complex statistical genetic concepts and findings to nontechnical stakeholders.
• Communicate and align with engineering and product teams and work towards achieving common understanding of needs and requirements.
• Prepare and present scientific publications reports and presentations to both internal and external audiences.

Skills Experience

• PhD (or Masters degree with extensive experience) in quantitative discipline such as Statistical Genetics Bioinformatics Computational Biology or a related field.
• Minimum of 5 years of experience (for Lead position) or at least 3 years of experience (for Senior position) in statistical genetics or a closely related discipline.
• Strong programming skills in languages such as R Python and experience with relevant bioinformatics tools and databases.
• Extensive experience with largescale genomic datasets (e.g. Open Target Genetics GWAS/eQTLCat) and biobanks (e.g. UKBiobank FinnGen)
• Experience in target validation procedures such as variant annotations (VEP) GO enrichment pathway enrichment PPIs.
• Experience in utilizing and understanding of onprem and/or cloud highperformance computing environments.
• Experience working with at least one cloud platform (Azure GCP Amazon AWS).
• Experience working with bioinformatics tools CLI/APIs (e.g. PLINK bcftools GATK etc.)
• Proven track record of accomplishment of conducting and publishing highquality research in statistical genetics.
• Excellent problemsolving skills and the ability to work independently and as part of a team.
• Effective communication skills with the ability to convey complex scientific concepts to a diverse audience.

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Remote Work :

No