Data Scientist

Energia
Belfast
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist (NLP & LLM Specialist)

Overview
  • Contract: Permanent - Full Time
  • Working Hours: 37 hours - Monday to Friday
  • Location: Newforge Lane, Belfast
Our Rewards
  • Up to 10% Annual Bonus
  • Annual Cost of Living Review
  • 25 Days Holiday per year + 11 stats and opportunity to buy more holidays
  • Family Friendly Policies
  • Enhanced Maternity and Paternity Pay
  • Health Care
  • Sick Pay
Your new opportunity

Energia Group seeks to appoint an experienced Data Scientist to join our growing Group Data Office, reporting to the Head of Data & Analytics. You will work closely with your colleagues in our Group Data Office within Technology & Innovation (T&I), and with senior managers and teams across our businesses to identify, prioritise, plan, and execute on opportunities to derive business value from data. You will bring up-to-date technical knowledge in data science and a passion for innovation and change; our business teams will bring an understanding of transformational outcomes they wish to achieve and their priorities; together you will forge a successful partnership to deliver data-led change that will benefit for our business and customers.

Your work will involve problem definition, problem solving, and effective working with colleagues. You will lead on problem structuring, data sourcing and transformation, model building and testing, model deployment to production, and post-production evaluation and updating. Your technical expertise will cover the full spectrum data science techniques (statistical models, Machine Learning, Artificial Intelligence), development principles and best practices, and key software tools. Some solutions may require descriptive analytics for business insights, so your knowledge of reporting tools Power BI will be useful.

What You'll Need
  • A bachelors degree in computer science, data science, engineering, or related field.
  • Extensive post-academic experience in Data Analytics, Data Science and/or Machine Learning; with a proficiency in SQL, Python and Databricks.
  • Experience and knowledge of statistical modelling techniques such as GLM regression, logistic regression, log-linear regression, deep learning, Bayesian methods, bagging & boosting techniques, clustering, sampling, variable selection, feature engineering and extraction, developing ML (Machine Learning) pipelines in data factory - MLOps, A/B experimentation, Git, model monitoring and leader board maintenance with MLFlow, code exception handling, code modularisation and unit testing.
  • Experience of AI/GenAI development frameworks, such as Azure AI Foundry.
  • Experience working in an agile manner, constantly seeking the most efficient methods to deliver against business objectives.

If you are enthusiastic about this role but dont meet every single requirement, we still encourage you to apply. Your past experiences might be the perfect match for this or other positions, making you the unique talent were looking for.

Learning & Development
  • Career Progression and Development - Almost 100% of our promotions are from within the organisation
  • Wellbeing Fund - to be used towards anything that energises you from Gym membership to Reflexology
  • Mentoring Scheme
  • Virtual Gym Membership
Social Opportunities
  • Sports and Social Club
  • Allyship@work
  • Women's Network
  • Green Team
  • Volunteering days and more
  • 87% of employees would recommend Energia Group to friends and family as a great place to work!
  • You will be joining a team where our company values of Trustworthy, Dynamic, Resourceful and Community Focussed are at the heart of all our efforts
  • You will have a variety of development opportunities available including internal L&D courses, mentoring, external networking, year-round learning and support for professional subscriptions
  • We also offer an annual Bonus of up to 10%, an award-winning wellbeing programme including a counselling service, sports and social clubs, a Wellbeing Fund to be used towards anything that energises you, generous holiday allowance with opportunity to buy more and Health Cash Plans.
  • Does Energy Move You? APPLY NOW!!!

NB: If you are successful in this role you will be required to carry out a Basic Access NI check. A criminal record will not necessarily be a bar to obtaining a position. Our Access NI Statement Policy and Recruitment of Ex-Offenders Policy will be provided upon request to HR.

Skills

IT Data Data Analytics

Benefits

Laptop Paid Holidays Parking Pension Fund


#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.