Naimuri - Senior Data Scientist

QinetiQ

Manchester

1 month ago

Applications closed

Join the team at Naimuri, a part of QinetiQ in Salford Quays, Manchester.

Job Details

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

About Naimuri

Naimuri is a technology startup that has grown into a key player in Manchester’s tech ecosystem. Our name, meaning “not overburden” in Japanese, reflects our commitment to delivering lightweight, agile solutions.

About The Team

The Data Capability team offers a collaborative environment focused on analysing data, designing solutions, and delivering effective data‑driven projects. It is a rapidly growing team that values continuous learning and shared expertise.

Responsibilities

Analyse customer requirements for long‑term projects and new bid work, uncovering opportunities to leverage data.
Modelling customer data, performing statistical analyses, designing cleansing, transformation, and normalisation processes, and extracting features.
Visualise, present, and communicate data analyses and modelling results to customers and project leads.
Engineered platforms, databases, and data pipelines as part of broader delivery solutions.
Train, deploy, and maintain ML/AI models, supporting transfer learning and feature extraction.
Write or support software solutions that implement data science models, tools, and techniques.

Qualifications

Significant industry experience as a data scientist with passion for data science practices.
Experience leading a team or project, mentoring others.
Conscientious, curious, and scientific approach to work.
Strong analytical and problem‑solving skills, able to design and develop innovative solutions.
Excellent communication skills for presenting complex ideas to diverse audiences.
Deep dives into data, presenting results using Jupyter notebooks.
Experience designing ingestion pipelines in Python and cloud solutions (AWS, Azure, GCP).
Full ML/AI lifecycle experience, including training, evaluation, and automated pipeline deployment.
Experience shaping organisational data science strategy and executing experiment plans.

Nice to Haves

Specialisms: Data Synthesis, Test and Evaluation, AI Assurance, Knowledge Graphs, Data Governance, or Deepfake Detection.
Python‑based applications or API development.
Degree in data science, physics, computational science, mathematics, or statistics.

Location

Head Office: Salford Quays, Manchester. Hybrid working: up to 1–2 days per week on site, with flexibility to work from home.

Pay & Benefits

Competitive base salary, industry‑standard rates.
Full‑time 37.5‑hour week, with flexible hours and part‑time options.
Core hours: 10:00‑15:00; office: 7:30‑18:00 Monday‑Friday.
Benefits: Flexible hybrid work, performance‑related bonus, pension matched 1.5x up to 10.5%, AXA Group medical cover, personal training budget, holiday buy‑back, flexible benefits scheme.

Recruitment Process

We will discuss the process with you once you apply. We support any accessibility or neurodiversity requirements throughout recruitment.

EEO Statement

We are committed to building an inclusive, safe and supportive environment that allows everyone to do their best work.

#J-18808-Ljbffr

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

Industry Insights

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

Feb 10, 2026

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.

Feb 3, 2026

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.

Jan 23, 2026

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.