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 opportunity to impact scientific research.
- Other info: Dynamic role with opportunities for growth in a fast-paced environment.
- Why this job: Join a cutting-edge team and contribute to groundbreaking discoveries in healthcare.
- Qualifications: Strong Python skills and experience with bioassay or experimental biology data.
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 employer: Harnham
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 while working 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 impactful careers in scientific research.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Scientific Data Scientist
✨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 role.
✨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
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your Python skills and experience with bioassay data. We want to see how your background aligns with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re excited about this role and how your scientific expertise can contribute to our data-driven discovery platform. Keep it engaging and personal!
Showcase Your Technical Skills:Don’t forget to mention your experience with tools like Pandas, NumPy, and any visualisation libraries you’ve used. We love seeing candidates who are not just familiar with these tools but have actually applied them in real-world scenarios.
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’s super easy!
How to prepare for a job interview at Harnham
✨Know Your Python Inside Out
Make sure you brush up on your Python skills, especially with libraries like Pandas, NumPy, and SciPy. Be ready to discuss how you've used these tools in past projects, particularly in building analytical pipelines or working with bioassay data.
✨Understand the Science Behind the Data
Familiarise yourself with the biological concepts relevant to the role, such as dose-response modelling and curve fitting. Being able to explain these concepts clearly will show that you can bridge the gap between wet-lab scientists and computational workflows.
✨Prepare for Technical Questions
Expect to face technical questions that test your understanding of statistical modelling and data validation. Practice explaining your thought process when developing QC frameworks or normalisation techniques, as this will demonstrate your analytical mindset.
✨Showcase Your Collaboration Skills
Since you'll be working closely with wet-lab scientists, be prepared to discuss how you've successfully collaborated in the past. Share examples of how you translated scientific requirements into computational workflows, highlighting your communication skills and teamwork.
