Lead Biomedical Data Scientist

Developing novel analytical pipelines, building machine learning models, custom bioinformatics scripting, and orchestrating complex data workflows.

Statistical analysis, advanced data visualisation, developing interactive dashboards for biological data, and specific bioinformatics packages.

Designing, building, and maintaining complex, containerised (Docker/Singularity) Nextflow/Snakemake pipelines for robust and reproducible bioinformatics workflows across the team.

Writing scripts to provision and manage cloud resources (e.g., EC2, Batch, S3/GCS, Lambda) for large-scale genomic analyses, optimising job scheduling and resource allocation to manage costs.

Designing database schemas, writing complex queries with joins and window functions, and optimising query performance on large-scale clinical and genomic datasets.

Building complex, interactive dashboards for exploratory data analysis, communicating key biological insights to diverse audiences, and tracking research programme progress.

Managing complex codebases, leading team-based development, implementing robust version control strategies for all analytical assets, and facilitating collaborative coding.

• VP of Research & Development
• Heads of Therapeutic Areas (e.g., Oncology, Neuroscience)
• Wet Lab Team Leads (e.g., Head of Genomics, Proteomics)
• Clinical Operations Leads
• IT Infrastructure Team
• Bioinformatics Core Facility

• Contract Research Organisations (CROs) for data generation
• Academic Collaborators (for joint research projects)
• Key Technology Vendors (e.g., cloud providers, software vendors)
• Regulatory Affairs (for data traceability standards)

As machine learning models become more central to drug discovery, we need robust systems to deploy, monitor, and maintain them in a regulated environment. This isn't just about building a model; it's about ensuring it works reliably in production, tracks its performance, and can be easily updated or retrained.

Model Versioning & Registry · Automated Model Deployment · Model Monitoring & Drift Detection · Explainable AI (XAI) for Biology

• This week: Research MLOps frameworks like MLflow or Kubeflow and understand their core components.
• This month: Implement a basic model registry for one of your team's existing machine learning models.
• Month 2: Design a monitoring dashboard to track the performance of a deployed model, looking for signs of data drift or concept drift.
• Month 3: Lead a workshop for your team on XAI techniques and how to apply them to biological models, focusing on interpretability for scientific insights.

Quick win: Start documenting the lineage of your current models more rigorously, including data sources, preprocessing steps, and hyperparameter tuning. This is the first step towards MLOps.

The sheer volume and complexity of multi-omics data continue to explode. We need Lead Scientists who can design and optimise cloud architectures that are not only powerful enough to process petabytes of data but also cost-effective and secure. This goes beyond just submitting jobs; it's about designing the entire computational ecosystem.

Serverless Compute for Bioinformatics · Data Lakehouse Architectures · Cost Optimisation Strategies · Security & Compliance in the Cloud

• This week: Review our current cloud spend and identify one area where costs could be reduced by 5-10% without impacting performance.
• This month: Complete an advanced course or certification in cloud architecture (e.g., AWS Certified Solutions Architect - Associate).
• Month 2: Design a proof-of-concept for a serverless bioinformatics workflow, demonstrating its cost-effectiveness and scalability.
• Month 3: Lead a review of our current data storage strategy in the cloud, proposing optimisations for cost and access speed for large-scale omics data.

Quick win: Familiarise yourself with your cloud provider's cost explorer tools and start tracking your team's compute and storage expenses more actively. Small changes can add up.