Lead Data Science Researcher

Teamtailor
Glasgow
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Data Science Researcher

Lead Data Science Researcher

Lead Data Analyst - Quant Hedge Fund

Lead Quantitative Researcher - Systematic Commodities

Lead Quantitative Researcher - Systematic Commodities

Head of Quantitative Analysis


A software development company is looking for a talented, long-term Lead DS Researcher. 

The company is a team of experts providing analytical services to healthcare clients. You will join an international team of first class professionals who are passionate to create products that improve quality of medical services. 

We’re looking for a Lead Data Science Researcher who thrives in research-heavy environments and enjoys exploring uncharted territory with the support of a strong technical team. You will lead a compact team of two data scientists, guiding them on high-impact research initiatives and experimental projects. Your role will involve pushing the boundaries of applied machine learning — especially in the context of medical and clinical data — and turning complex problems into innovative solutions.

This is a unique opportunity to drive forward new ideas and applications, not just optimize existing ones.

What we’re looking for:

  • Exceptional analytical and statistical skills-comfortable with uncertainty, inference, and experimentation;
  • Strong background in different areas of ML (traditional classification and regression techniques, recommender systems, text data, clustering, etc.);
  • Solid experience with deep learning frameworks like PyTorch or TensorFlow;
  • Excellent Python skills (beyond Jupyter Notebooks) - ability to build clean, testable, production-ready code;
  • Familiarity with medical or life science data is a strong plus;
  • Expertise in SQL, Pandas, Scikit-learn, and modern data workflows;
  • Comfortable working in Google Cloud Platform (GCP) environments.

Bonus points for experience with:

  • State-of-the-art NLP models, Transformers, Agentic Approaches for mixed (temporal and text) data analysis and summarization;
  • Experience with pipeline orchestration tools like Airflow, Argo, etc.;
  • Proven Experience with Anomaly Detection and Forecasting with explainability for temporal and mixed data;
  • Intermediate+ English — ability to participate in written discussions with international teams and clients.

Benefits: 

  • Join a mission-driven team working at the intersection of data, medicine, and impact;
  • Work on meaningful challenges with long-term value for public health and healthcare quality;
  • Collaborate with top-tier experts in a culture that values curiosity, autonomy, and innovation;
  • Fully remote-friendly setup with flexibility and trust at the core.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Quantum-Enhanced AI in Data Science: Embracing the Next Frontier

Data science has undergone a staggering transformation in the past decade, evolving from a niche academic discipline into a linchpin of modern industry. Across every sector—finance, healthcare, retail, manufacturing—data scientists have become indispensable, leveraging statistical methods and machine learning to turn raw information into actionable insights. Yet as datasets grow ever larger and machine learning models become more computationally expensive, there are genuine questions about how far current methods can be pushed. Enter quantum computing, a nascent but promising technology grounded in the counterintuitive principles of quantum mechanics. Often dismissed just a few years ago as purely experimental, quantum computing is quickly gaining traction as prototypes evolve into cloud-accessible machines. When paired with artificial intelligence—particularly in the realm of data science—the results could be game-changing. From faster model training and complex optimisation to entirely new forms of data analysis, quantum-enhanced AI stands poised to disrupt established practices and create new opportunities. In this article, we will: Explore how data science has reached its current limits in certain areas, and why classical hardware might no longer suffice. Provide an accessible overview of quantum computing concepts and how they differ from classical systems. Examine the potential of quantum-enhanced AI to solve key data science challenges, from data wrangling to advanced machine learning. Highlight real-world applications, emerging job roles, and the skills you need to thrive in this new landscape. Offer actionable steps for data professionals eager to stay ahead of the curve in a rapidly evolving field. Whether you’re a practising data scientist, a student weighing up your future specialisations, or an executive curious about the next technological leap, read on. The quantum era may be closer than you think, and it promises to radically transform the very fabric of data science.

Data Science Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Data science has become an indispensable cornerstone of modern business, driving decisions across finance, healthcare, e-commerce, manufacturing, and beyond. As organisations scramble to capitalise on the insights their data can offer, data scientists and machine learning (ML) experts find themselves in ever-higher demand. In the UK, which has cultivated a robust ecosystem of tech innovation and academic excellence, data-driven start-ups continue to blossom—fuelled by venture capital, government grants, and a vibrant talent pool. In this Q3 2025 Investment Tracker, we delve into the newly funded UK start-ups making waves in data science. Beyond celebrating their funding milestones, we’ll explore the job opportunities these investments have created for aspiring and seasoned data scientists alike. Whether you’re interested in advanced analytics, NLP (Natural Language Processing), computer vision, or MLOps, these start-ups might just offer the career leap you’ve been waiting for.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.