x 10 Senior Data Scientists/Data Engineers Needed (multiple Roles) - DV/SC Cleared

Areti Group | B Corp
London
2 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Analytics and Insights

2 x Data Analyst - Local Authority

2 x Data Analyst - Local Authority

Data Warehouse Manager - Fabric Lakehouse, PySpark

Data Warehouse Manager - Fabric Lakehouse, PySpark ETL

Data Engineer - Databricks

Job Description

Areti is seeking x10 Senior Data Scientists/Data Engineers (Palantir, Python, Data Science) to work for a series A Funded tech start-up business based in the London area a very quick start.


YOU MUST BE DV OR SC CLEARED


You will be working on a number of large Defence and Government projects and be given the opportunity to lead a number of high-profile programs.


The Data Scientists will apply their technical skillsets and knowledge to solve exciting real-world problems in a small and dynamic company.


You will work as part of a dynamic and close-knit team building new and proprietary approaches to solving multiple problems across the industry. The ideal candidates will have a skillset to include the following:

  • Experience in working as a Data Scientist/Data Engineer
  • A understand of Palantir would be a huge +
  • Experience of completing projects to deadline.
  • Experience in Deep Learning and DL frameworks such as Tensorflow/Pytroch
  • Deploying ML models
  • Good command of Python and use of libraries for data science – scikit-learn, NumPy, matplotlib
  • Relation database experience with data manipulation skills in SQL and large “Big Data”...

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.