Data Scientist

Multiverse
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist (NLP & LLM Specialist)

Multiverse is the upskilling platform for AI and Tech adoption. We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today’s workforce.


Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance.


In June 2022, we announced a $220 million Series D funding round co‑led by StepStone Group, Lightspeed Venture Partners and General Catalyst. With a post‑money valuation of $1.7bn, the round makes us the UK’s first EdTech unicorn.


But we aren’t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We’re building a world where tech skills unlock people’s potential and output.


Join Multiverse and power our mission to equip the workforce to win in the AI era.


As a Senior Data Scientist, you will play a pivotal role in steering our Data Science team towards achieving strategic objectives. Your expertise will guide collaborative efforts in product and backend services alike, whilst collaborating with product experts, engineers and other stakeholders from across the organization.


You’ll leverage your advanced analytical skills and understanding of AI and machine learning to drive impactful insights and foster innovative solutions. This dynamic role demands a balance of creativity and analytical rigor within a fast‑paced environment, ensuring quick learning and iteration based on user feedback.


What you’ll focus on

  • Translate complex stakeholder queries and hypotheses into actionable analyses, experiments and AI/ML model requirements.
  • Develop a comprehensive understanding of our data lineage and sources, addressing and mitigating sampling and analytical biases.
  • Oversee the productionisation of analyses and models, ensuring their seamless operation at scale by adhering to software engineering best practices.
  • Drive targeted exploration of our data landscape, ideating and implementing innovative ways to use data for enhancing user engagement on our products
  • Build out our knowledge graph capability for underpinning AI/ML models and agentic workflows
  • Proactively monitor and refine analyses and models, optimizing effectiveness and efficiency while minimizing biases and operational challenges.
  • Evaluate and validate scalable methodologies for data collection and processing, ensuring robust practices are in place.
  • Communicate actionable insights to stakeholders at all levels, bridging the gap between technical concepts and business objectives.

What we’re looking for
Required

  • 5+ years of data science/machine learning experience, with a proven track record in leading complex data projects.
  • Extensive experience in deploying supervised/unsupervised machine learning algorithms and AI tools into production, delivering scalable and effective solutions.
  • Strong proficiency in Python and key libraries commonly used in machine learning (e.g., NumPy, Pandas, Scikit‑Learn, PyTorch, Langchain).
  • Advanced working knowledge of SQL
  • Experience with GitHub for version control.
  • Demonstrated experience productionising ML models and analytic outputs within cloud environments (e.g., AWS, Azure).
  • Understanding of best practices in data protection and information security.
  • A tenacious, curious, and pragmatic approach to problem solving, focusing on creating usable, scalable outputs.
  • Exceptional attention to detail and a strong analytical mindset.
  • A growth‑oriented attitude and a passion for continuous learning and professional development.
  • A commitment to Multiverse’s mission and values.

Non‑Required (But Desirable)

  • Familiarity with the education/skills sector
  • Understanding of the semantic web, knowledge graphs and/or network analytics
  • Direct experience with CI/CD practices (e.g., GitHub Actions).
  • Knowledge of infrastructure as code tools (e.g., Terraform).
  • An advanced degree in a numerical, engineering or related discipline.

Benefits

  • Time off - 27 days holiday, plus 7 additional days off: 1 life event day, 2 volunteer days and 4 company‑wide wellbeing days and 8 bank holidays per year
  • Health & Wellness- private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Gympass and access to Spill - all in one mental health support
  • Hybrid work offering - and the opportunity to take part in our work‑from‑anywhere scheme
  • Team fun - weekly socials, company wide events and office snacks

Our commitment to Diversity, Equity and Inclusion

We’re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change. Read our Equality, Diversity & Inclusion policy here.


Right to Work

Do you have the right to work in the UK? Unfortunately, at this time we cannot offer sponsorship for this role and we cannot consider overseas applications.


Safeguarding

All posts in Multiverse involve some degree of responsibility for safeguarding. Successful applicants are required to complete a Disclosure Form from the Disclosure and Barring Service ("DBS") for the position. Failure to declare any convictions (that are not subject to DBS filtering) may disqualify a candidate for appointment or result in summary dismissal if the discrepancy comes to light subsequently.


Seniority level

Mid‑Senior level


Employment type

Full‑time


Job function

Engineering and Information Technology


Industries

Higher Education



#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.