Senior Research Data Scientist

UCL
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Economist/Data Scientist

Senior Data Scientist (Document Search)

Senior Data Scientist, Quantitative Biosciences

Senior Data Scientist (Document Search)

Senior Data Scientist (Document Search)

The UCL Centre for Advanced Research Computing (ARC) is UCL's new institute for infrastructure and innovation in digital research - the supercomputers, datasets, software and people that make computational science and digital scholarship possible. We are an innovative hybrid: a professional services department that delivers reliable and secure infrastructure and services to UCL research groups, and a laboratory for research and innovation in the application of advanced computational and data intensive research methods, working in partnership with academics from all fields.

We are a home for the research technology professionals - research software engineers, HPC systems engineers, dev-ops specialists, data engineers, data scientists and data stewards - who support and collaborate in the delivery of UCL research.

About the role

You will be part of ARC's community of staff scientists and research technology professionals, both delivering the services and systems which make data and compute-intensive research possible, and discovering and innovating new tools, practices, and systems in this field.

For this post, we would like to hear from applicants with expertise in LLMs and DevOps.

Our positioning as a hybrid of a research institute and service centre means these activities will be synergistic and you will play a key role in developing and maintaining technical or domain specialisms. This could mean building on your background to remain close to the research life of one or more of the academic disciplines we collaborate with (e.g. via honorary membership of an academic department) or engaging closely with the technology professional communities related to a platform, tool, or language that you focus on.

Alongside a hugely talented team, you will help to define a portfolio of responsibilities, a mixture of service delivery, research, innovation, and teaching activities according to your own preferences and skills, and appropriate to your level of seniority.

A fantastic opportunity!

About you

As the ideal candidate you will have a strong mix of the following skills and experience:

  • PhD Degree OR equivalent professional expertise appropriate to the role. This is a role in a research community and we are great believers in the value of research degrees. However, we are also very committed to building a diverse community, with important differences from traditional academia. A PhD is by no means the only way to gain experience of contributing to research through technology. About half our current staff have PhDs.
  • Experience with LLMs and DevOps
  • Experience with the core tools of data science and analytics, in a programmatic framework such as R, Python, or Julia.
  • Advanced knowledge of statistics and mathematics applied to acquiring reliable insight from data.
  • Experience with at least one specialist technique or tool in data analytics, such as natural language processing, neural networks, Bayesian inference, Gaussian processes, or techniques associated with a particular research field such as bioinformatics tools.

What we offer

As well as the exciting opportunities this role presents we also offer some great benefits:

  • 41 Days holiday (including 27 days annual leave, 8 bank holidays, and 6 closure days)
  • This post is eligible for USS Pension Scheme defined benefit scheme
  • Staff contribute 6.1% and UCL will contribute 14.6%
  • Life insurance is included (3x salary)
  • Cycle to work scheme and season ticket loan
  • On-site gym
  • Enhanced maternity, paternity and adoption pay
  • Employee assistance programme
  • Discounted medical insurance

Our commitment to Equality, Diversity and Inclusion

As London's Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world's talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong.

We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL's workforce.

These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women.

Our department is working towards an Athena SWAN award. We are committed to advancing gender equality within our department.

#J-18808-Ljbffr

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.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.