Lead Data Scientist

Data Science Festival
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data ScientistSalary: £115,000 – £125,000Location: London/Hybrid

Data Idols are partnered with a leading technology distributor that is continuing to invest heavily in data. They are looking for a Lead Data Scientist who can act as the senior technical expert within the team, someone who delivers high-impact models, sets technical standards, and leads complex projects through to production.

The Opportunity

As a Lead Data Scientist, you’ll be the go-to technical authority, taking ownership of challenging modelling work and driving end-to-end delivery. You’ll work deeply hands-on, developing advanced models, improving existing pipelines, and ensuring solutions are scalable and production-ready.

You’ll collaborate closely with the Head of Data Science, shaping the technical approach, advising on best practices, and leading major initiatives. While not a people manager, you will support and mentor others by setting the bar for technical excellence and helping guide their development.

This role is ideal for someone who thrives as a senior IC and wants to stay close to the code and modelling while having a strong voice in technical decision-making.

Skills and Experience
  • Extensive experience building, validating, and deploying machine learning models into production
  • Strong hands-on Python and SQL skills
  • Experience working in cloud environments (GCP preferred)
  • Deep understanding of experimentation, evaluation, and scalable ML design
  • Ability to mentor others and influence technical direction without formal line management

If you’re looking for a role where you can remain hands-on while owning major technical challenges, please submit your CV for initial screening.

Lead Data Scientist


#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.