Senior Data Scientist

SF Recruitment (Tech)
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist - Fintech | First Permanent DS Hire

£75,000-£85,000 + benefits | Fully Remote (UK)
Direct report to CDO | Build the DS capability | FCA-regulated product

This is a rare opportunity to join a fast-growing, FCA-regulated fintech as their first permanent Senior Data Scientist, shaping a brand-new data science capability from the ground up.

The business has built a highly successful SaaS platform in the Fintech space. With strong investment and FCA approval now secured, they're moving firmly into the data products and insights space - turning the rich consumer financial and behavioural data they hold into real intelligence, new models, and new customer offerings.

Things expected from you..

Sets the standard
Builds the capability
Shapes the roadmap
Becomes the go-to person for modelling, insights and DS foundations

If you're someone who enjoys autonomy, variety, being close to stakeholders, and having genuine influence in a startup that's now scaling - this is the ideal next step.

This is a genuinely varied and hands-on role where you'll:

Build and scale data science and modelling foundations in Databricks

Work with financial, consumer and behavioural data to create new models & scorecards

Design MI dashboards and reporting to help the business understand its own data

Collaborate closely with Product, Finance, Customer Ops and the senior leadership team

Evaluate where ML/AI can enhance the core SaaS platform

Present insights and model outputs clearly to non-technical stakeholders (including board/NEDs)

Influence how the data function grows over the next 12-24 months

This is the start of the data and analytics function - you won't just inherit a roadmap; you'll help write it.

You'll thrive here if you are:

A strong hands-on Data Scientist with experience in FS/fintech or regulated environments

Confident working with Python, SQL and Databricks

Capable of building predictive models, scorecards and ML components

Comfortable creating dashboards/MI to support internal understanding

Excited by a startup environment where you'll wear different hats

Able to communicate clearly to senior and non-technical audiences

Looking for ownership and long-term progression into Head of DS

Why this role is genuinely exciting

You're the first permanent hire - you shape how DS works here

Direct line to the C-suite, not buried in a data pod

True autonomy: you influence strategy, tooling, roadmap and delivery

Visible across the entire business, including investors & NEDs

The company is moving from SaaS - data products, meaning greenfield work

Clear long-term upward path (team will grow over the next 12-24 months) - Underneath you ideally!

This is the role for someone who wants more than just building models - someone who wants to make their mark and grow with a FinTech entering its next phase.

TPBN1_UKTJ

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.