At a Glance
- Tasks: Design and maintain analytical pipelines for data-driven drug discovery.
- Company: Innovative tech-enabled drug discovery company with a focus on collaboration.
- Benefits: Competitive daily rate, remote/hybrid work, and contract flexibility.
- Other info: Dynamic role with opportunities to work alongside wet-lab scientists.
- Why this job: Make a real impact in drug discovery using your coding and scientific skills.
- Qualifications: Strong Python skills and experience with bioassay data analysis.
A technology-enabled drug discovery company is looking for a Senior Scientific Data Scientist to design, build and maintain the analytical pipelines that power its data-driven discovery platform. The business combines automated wet-lab experimentation, proprietary analytical pipelines and deep scientific expertise to generate high-quality, reproducible biological datasets at scale. This data supports AI-driven discovery work across areas including oncology, immunology and neurodegeneration.
This role would suit a scientist-turned-coder or scientific data scientist with strong Python skills, statistical modelling experience and a background working with bioassay or experimental biology data.
The Role
- You will build and improve Python-based analytical pipelines for diverse bioassay datasets, including biochemical, biophysical and cell-based assays.
- You will work closely with wet-lab scientists to understand assay logic, experimental design and data handover processes, then translate those workflows into automated, reproducible analysis pipelines.
- The work will involve dose-response modelling, curve fitting, QC, normalisation, plate-level statistics and data validation, helping scientists generate clearer, faster and more reliable insight from complex experimental data.
Key Responsibilities
- Build, maintain and extend Python-based analytical pipelines for bioassay datasets.
- Develop statistical and modelling workflows, including dose-response modelling, 4PL curve fitting and mechanistic models.
- Build QC, normalisation and data validation frameworks for experimental biology data.
- Support plate-level statistics and analysis of high-throughput assay outputs.
- Translate wet-lab scientific workflows into reproducible automated pipelines.
- Improve data structures, file preparation and analysis readiness.
- Partner with wet-lab scientists to understand assay design and experimental logic.
- Clearly communicate analytical choices, QC decisions and modelling approaches.
- Build robust, documented and reusable scientific software.
Essential Skills
- Strong Python experience for scientific computing.
- Experience with Pandas, NumPy, SciPy and visualisation libraries such as Matplotlib or Seaborn.
- Experience analysing bioassay, assay or experimental biology data.
- Understanding of dose-response modelling, curve fitting, 4PL, EC50 / IC50 or similar.
- Experience with QC, normalisation, data validation or plate-level statistics.
- Experience building reproducible analytical pipelines or automated workflows.
- Good software engineering habits, including Git, documentation and reproducibility.
- Ability to work closely with wet-lab scientists and translate scientific requirements into computational workflows.
Nice to Have
- Experience with 384-well or 1536-well plate-based assay data.
- lmfit, Pandera, scikit-learn, Biopython or RDKit.
- Background in drug discovery, biotech, pharma or CRO environments.
- Experience across biophysics, cell biology, enzymology, oncology or immunology.
- Experience with high-throughput screening, assay validation or automated lab platforms.
The Candidate
The ideal candidate will be scientifically fluent and technically hands-on. You may come from a scientific data science, computational biology, bioinformatics, research software engineering or scientific software background. You should be comfortable working with complex experimental data, applying statistical rigour and building tools that improve speed, reproducibility and scientific insight across a multidisciplinary team.
Senior Scientific Data Scientist in Didcot employer: Harnham - Data & Analytics Recruitment
As a technology-enabled drug discovery company based in Oxford, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to thrive. With a focus on professional growth, we offer opportunities for skill development in cutting-edge scientific data analysis and the chance to work alongside leading experts in the field. Our commitment to flexibility through remote and hybrid working arrangements ensures a healthy work-life balance, making us an excellent employer for those seeking meaningful and rewarding careers in scientific data science.
Contact Details:
Harnham - Data & Analytics Recruitment Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Senior Scientific Data Scientist in Didcot
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend relevant meetups or webinars, and don’t be shy about asking for introductions. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects, especially those related to bioassay data or analytical pipelines. We recommend using platforms like GitHub to share your work; it’s a great way to demonstrate your coding chops and make a lasting impression.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you’ll need to communicate effectively with wet-lab scientists. We suggest doing mock interviews with friends or using online resources to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive and engaged with our platform. So, go ahead and take that step!
We think you need these skills to ace Senior Scientific Data Scientist in Didcot
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Senior Scientific Data Scientist role. Highlight your Python expertise, experience with bioassay data, and any relevant projects that showcase your ability to build analytical pipelines.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're the perfect fit for this role. Share specific examples of how you've tackled similar challenges in the past, especially around dose-response modelling and working with wet-lab scientists.
Showcase Your Technical Skills:Don’t forget to mention your proficiency with tools like Pandas, NumPy, and visualisation libraries. We want to see how you’ve used these in real-world scenarios, so include any relevant projects or achievements that demonstrate your technical prowess.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company and culture!
How to prepare for a job interview at Harnham - Data & Analytics Recruitment
✨Know Your Tech Inside Out
Make sure you brush up on your Python skills and get familiar with libraries like Pandas, NumPy, and SciPy. Be ready to discuss how you've used these tools in past projects, especially in building analytical pipelines or working with bioassay data.
✨Understand the Science
Since this role involves close collaboration with wet-lab scientists, it's crucial to have a solid grasp of experimental design and assay logic. Prepare to explain how you would translate scientific workflows into automated analysis pipelines.
✨Showcase Your Problem-Solving Skills
Be prepared to discuss specific challenges you've faced in previous roles, particularly around dose-response modelling or data validation. Highlight how you approached these problems and the impact of your solutions on the overall project.
✨Communicate Clearly
Effective communication is key, especially when discussing analytical choices and QC decisions. Practice explaining complex concepts in simple terms, as you'll need to convey your ideas clearly to both technical and non-technical team members.
