Data Scientist

Entasis Partners
Manchester
9 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist (Mid Level)


Are you ready to make a meaningful impact in the mobile network industry?


We are looking for a talented Data Scientist to join a dynamic and innovative team, contributing to mobile network data analysis innovation while driving sustainability efforts. This fully remote role offers the chance to join a growing data analytics team within an ambitious and market-leading organisation.


Your Responsibilities

  • Apply data science and machine learning techniques to time-series data, delivering impactful insights for clients.
  • Collaborate with business analysts and software engineers to create cutting-edge solutions to industry challenges.
  • Design algorithms and experiments to support product development and enhance business intelligence.
  • Process, clean and verify data integrity while automating event and anomaly detection models.
  • Stay informed on technological advancements, proactively implementing new techniques to boost analytics capabilities.


About You

  • At least 2 years’ experience in data science or analytics roles.
  • Proficient in Python, SQL and data science tools.
  • Comfortable working with time-series data and applying machine learning techniques.
  • Strong statistics background and problem-solving abilities.
  • Independent and self-motivated with excellent communication and collaboration skills.
  • Telecom experience isn’t necessary - just bring your enthusiasm to learn and innovate.


Be part of a collaborative remote-working team with a global impact. You'll benefit from:

  • Flexible work arrangements, with regular in-person team meetups in London.
  • Generous benefits package including holiday allowance, pension contributions, equity options, and a budget for personal development.
  • Opportunities to work on exciting industry initiatives like carbon reduction and rural expansion.
  • Support for continuous learning and professional growth tailored to your interests.


Ready to apply?


If this role sounds like the perfect fit for your skills and ambitions, we'd love to hear from you. Apply now and be part of an innovative team transforming their sector!

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.