Scientific Manager - ML Engineer

Scientific Manager – Machine Learning Engineering (Computational Biology)

We are hiring a Scientific Manager to take ownership of the machine learning and experimental design pipelines behind our customer’s genomic target prediction work.

Much of this currently lives in individual scripts maintained by individual scientists. Your job is to turn it into an automated, version-controlled and reproducible platform the whole team can rely on – and to keep it scientifically sound by working shoulder to shoulder with our customer’s wet-lab and computational biology colleagues.

This is a hands-on engineering role in a scientific environment. You will spend more of your time building, automating and hardening pipelines than running individual analyses, so we are looking for someone who is as comfortable with Git, containers and workflow managers as with genomics data.

About Excelra

Excelra is a leader in biopharma data and analytics, that uniquely blends scientific understanding with technological expertise. This dual mastery empowers innovative drug discovery and development, transforming research data into actionable insights for groundbreaking discovery.

We have locations in Hyderabad (head offices – India), Ghent (Belgium), London (UK), Utrecht (The Netherlands), and Boston/SFO (US).

www.excelra.com

Responsibilities

• Automate and professionalize internal ML and experimental design pipelines for genomic target prediction.
• Migrate existing scripts and notebooks into a production-ready, version-controlled codebase.
• Reduce the manual input required for dataset preparation, settings configuration and pipeline execution.
• Integrate pipelines into existing digital infrastructure, including AWS and HPC environments.
• Implement version control, testing and reproducibility standards across ML workflows, and help the team adopt them.
• Collaborate closely with wet-lab and computational biology colleagues to make sure pipelines meet real scientific requirements.
• Run several projects in parallel, (re)assigning tasks as priorities shift, and present results concisely to stakeholders inside and outside your domain.

Requirements

• A PhD in bioscience engineering, bioinformatics, computer science or machine learning engineering – or an MSc with 5+ years of equivalent experience. You can demonstrate this through peer-reviewed publications, contributions to public code repositories or relevant project work.
• Strong Python, and the habit of writing code that colleagues can read, test and re-run.
• Proven experience taking analysis code into production: version control with Git, code review, packaging, containerization (Docker, Singularity/Apptainer) and automated testing or CI/CD.
• Hands-on experience with workflow orchestration – Nextflow, Snakemake or equivalent.
• Experience running workloads on cloud infrastructure (AWS) and/or HPC clusters (e.g. SLURM).
• Solid grounding in statistical modelling and machine learning applied to biological data, including model evaluation and experiment tracking.
• The ability to translate between engineering and bench science, and to work with scientists who are not engineers.
• You are eligible to live and work in Europe.
• You enjoy project-based work and have a strong analytical mindset.
• You enjoy working in a multidisciplinary environment with people of diverse professional backgrounds (IT, (molecular) biology, genomics, engineering, etc.).
• You are communicative and customer focused.
• You are fluent in English, both written and verbally.
• You take initiative and have an eye for new opportunities.

Preferred

• Experience with genomics and transcriptomics data (RNAseq, single-cell, spatial, WES/WGS, …).
• R alongside Python.
• Experiment tracking and model registry tooling (MLflow, Weights & Biases).
• Experience in a client-facing, consulting or CRO setting.

Benefits

• A challenging job environment with advancement opportunities.
• Flexible hybrid working in an international setting.
• A dynamic company with an open culture, easy communication, and an ambitious vision.
• A rich and global team with a mix of backgrounds, experiences, and expertise.
• Independence and flexibility.
• Opportunities to work on large-scale genomics datasets and cutting-edge sequencing technologies.
• An attractive salary package with extra-legal benefits.