Senior/Lead Health Data Scientist – Statistical Genetics

Optima Partners
Edinburgh
3 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Genomic Data Scientist - 2 Year FTC, Adult Population Genomics Programme (we have office locations in Cambridge, Leeds & London)

Senior Manager, Forward-Deployed Data Science

Senior Data Scientist

Senior Data Scientist & ML Engineer (f/m/d)

Senior Data Scientist (UK)

Portfolio Revenue & Debt Data Scientist

Senior/Lead Health Data Scientist – Statistical Genetics

We are an advanced data and business consultancy headquartered in Edinburgh, UK. We are a practitioner-led organisation that collaborates with top consumer brands to drive transformation and foster customer-centricity through our expertise in customer strategy, innovative design, and advanced data science and engineering.


In late 2023, we proudly launched our new division, bioXcelerate AI, which stands at the forefront of revolutionising life sciences and healthcare research. bioXcelerate AI uses state-of-the‑art data science and proprietary algorithms to accelerate the transformation of data into actionable insights, redefining industry standards. At bioXcelerate, we are fostering a scientific community; therefore, our scientists are exposed to a vast academic collaborations while delivering on pressing issues in the life sciences industry.


The opportunity

As part of our expansion, we are dedicated to advancing our capabilities in data science and statistical genetics. We are seeking an exceptional Data Scientist specialising in Statistical Genetics and Computational Biology to join our team. This role will be pivotal in driving our genetic research initiatives and contributing to cutting‑edge solutions that enhance our services.


What you will be doing

  • Design and conduct advanced statistical analyses of large-scale genetic and genomic datasets.
  • Ability to interpret the results and find tangible link between the outcomes and the methodology applied.
  • Develop and validate theoretically grounded methods 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.
  • Drive forward the development of the Innovation capabilities & lead the growth of the team
  • 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.
  • Communicate complex statistical genetic concepts and findings to non-technical stakeholders.
  • Communicate and align with engineering and product teams and work towards achieving common understanding of needs and requirements.
  • Ensure the deliverables follow a timely manner according to the scope pre-defined for individual projects.
  • Prepare and present scientific publications, reports, and presentations to both internal and external audiences.

What skills we would like you to have

  • PhD (or Master’s degree with extensive experience) in quantitative discipline such as Statistical Genetics, Bioinformatics, Computational Biology, or a related field.
  • Minimum 3 years of experience 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 large-scale 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 working with at least one cloud platform (Azure, GCP, AWS).


  • Proven track record of accomplishment of conducting and publishing high-quality research in statistical genetics.
  • Effective communication skills with the ability to convey complex scientific concepts to a diverse audience.
  • Apply statistical genetics methodologies such as genome-wide association studies (GWAS), meta-analysis, polygenic risk scoring, heritability.
  • Apply genetics causal inference methodologies such as finemapping, colocalization and Mendelian randomization.
  • Scientifically support ideation, design, development and maintenance of the large-scale pipelines and workflows.
  • Ensure robust data processing pipelines and workflows for handling large-scale genomic data.
  • Familiarity with target validation approaches such as ontology & pathway enrichment, gene and protein annotations, disease-phenotype associations.

What we offer

  • Competitive base salary
  • Inclusion in our annual discretionary bonus plan with an on-target performance bonus of up to 15%.
  • Inclusion in bioXcelerate’s R&D incentive scheme which rewards members for their contribution to innovations that add value to the Optima portfolio.
  • Up to 37 days holiday, inclusive of personal and public allocations, of which 7 are fixed days (Christmas & New Year) and 30 are floating, taken at your discretion subject to client scheduling and line manager approval.
  • Private medical insurance (single cover).
  • Group life & income protection insurance.
  • Salary Sacrifice Pension Scheme after 3 months employment
  • Access to over 1,000 perks and discounts via an employee discount portal.
  • Dedicated development time, tools and funding to support personal and professional development.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.