Naimuri - Senior Data Scientist

Van Arendonk Makelaardij
Manchester
1 month ago
Applications closed

Naimuri - Senior Data Scientist role at Van Arendonk Makelaardij


Job Title: Senior Data Scientist
Job Location: Salford Quays, Manchester
Job Type: Permanent, Full-time
Job ID: SF19357


Naimuri is offering the chance to help make the UK a safer place through innovation. We partner with government and law enforcement on some of the most challenging data and technology problems, and we're looking for a Senior Data Scientist to join our mission. We strongly encourage candidates of all different backgrounds and identities to apply. We are committed to building an inclusive, safe and supportive environment.


About Us

We’ve been around for about ten years, growing from a little‑known tech start‑up to a core of the Manchester tech ecosystem. The name Naimuri is Japanese: nai meaning “not” and muri meaning “overburden.” It guides everything we do, from technology to culture. Our business is focused on four cornerstones: Wellbeing, Empowerment, Perpetual Edge and Delivery.


About The Team

The Data capability team offers a unique opportunity to apply your skills to impactful projects. We analyse data, design solutions to data‑driven challenges, and make a real difference for our customers. We are passionate about continuous learning and fostering shared expertise.


Professional Responsibilities

  • Analyse product runs
  • Model customer data, perform statistical analyses, design cleansing, transformation and normalisation processes, conduct feature extraction/reduction.
  • Visualise and present analyses and analytics to customers and project leads.
  • Engineer platforms, databases, and data pipelines as part of broader delivery solutions.
  • Train (inc. transfer learning and feature extraction) and deploy ML/AI models for prediction, detection, classification.
  • Write or support software solutions that apply data science models, tools, and techniques.


#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.