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Flight Data AnalystOn site 5 days/Shift pattern£30,000 - £35,000 + OvertimeCapgemini are looking to recruit a Flight Data Analyst to join our team based in Brize Norton.Job PurposeSupport the A400M maintenance by capturing and transferring into MDS (Maintenance Data System) the maintenance information generated by the Part 145 during a mission. This covers the period prior to the aircraft departure...
Big Data :Are you excited about taking your technical career to new heights with a full-time, W-2 role as a consultant in a dynamic and rapidly growing company? If you are, let's get in touch - your interest is the first step to starting the conversation.What This Role Requires:· 1-4 years of programming experience after your degreeMust have coding experience...
Network Planning Manager – WestHours: 37.5 hours per week (flexibility required)Salary: Upto Circa £55k Basic and OTE £60k+ BenefitsWe are looking for a Network Planning Manager – West to join a well-established transport operator. This is a fantastic opportunity for an experienced network planner to oversee the development of timetables, duties, and rotas that evolve to meet customer and stakeholder...
Databricks Data Engineer: £60,000I am looking for a data engineer who has experience in Databricks, Azure, SQL, Python and Spark to join a well-established organisation who are currently expanding their data team.Our client is partnered with both Databricks and Microsoft and they deliver data solutions for a diverse range of clients.They operate with a hybrid working model, where employees are...
Embedded Linux Engineer – £75k – semi remote - OxfordHexwired Recruitment is recruiting for a rapidly expanding solutions provider based in Oxford now seeking an Embedded Linux Engineer to help deliver key projects within Audio and media solutions!The company are expanding to meet the demands of their clients and are seeking an Embedded Linux Engineer ideally with experience working within...
Lead Electronics Engineer – £65k - £80k – semi remote - WokingHexwired Recruitment is recruiting for a rapidly expanding solutions provider based in Woking now seeking a Lead Electronics Engineer to help deliver key projects for clients across a range of industries! You will be working as part of an experienced team to develop a range of bespoke products.The company...
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When CEOs need to unlock billion‑pound efficiencies or launch AI‑first products, they often call McKinsey & Company. What many graduates don’t realise is that behind every famous strategy deck sits a global network of data scientists, engineers and AI practitioners—unified under QuantumBlack, AI by McKinsey. From optimising Formula One pit stops to reducing NHS wait times, McKinsey’s analytics teams turn messy data into operational gold. With the launch of the McKinsey AI Studio in late 2024 and sustained demand for GenAI strategy, the firm is growing its UK analytics headcount faster than ever. The McKinsey careers portal lists 350+ open analytics roles worldwide, over 120 in the UK, spanning data science, machine‑learning engineering, data engineering, product management and AI consulting. Whether you love Python notebooks, Airflow DAGs, or white‑boarding an LLM governance roadmap for a FTSE 100 board, this guide details how to land a McKinsey data‑science job in 2025.
Data Science has evolved into one of the most popular and transformative professions of the 21st century. Yet as the demand for data-related roles expands, other fields—such as Data Mining and Business Intelligence (BI)—are also thriving. With so many data-centric career options available, it can be challenging to determine where your skills and interests best align. If you’re browsing Data Science jobs on www.datascience-jobs.co.uk, you’ve no doubt seen numerous listings that mention machine learning, analytics, or business intelligence. But how does Data Science really differ from Data Mining or Business Intelligence? And which path should you follow? This article demystifies these three interrelated yet distinct fields. We’ll define the core aims of Data Science, Data Mining, and Business Intelligence, highlight where their responsibilities overlap, explore salary ranges, and provide real-world examples of each role in action. By the end, you’ll have a clearer sense of which profession could be your ideal fit—and how to position yourself for success in this ever-evolving data landscape.
Data science has rapidly evolved into a driving force for businesses and organisations worldwide. In the United Kingdom, companies across sectors—including finance, retail, healthcare, tech start-ups, and government agencies—are turning to data-driven insights to boost competitiveness and innovation. Whether you specialise in statistical modelling, machine learning, or advanced analytics, data scientists are in high demand throughout the UK’s vibrant tech ecosystem. If you’re an international data scientist aiming to launch or grow your career in the UK, one essential part of the journey is navigating the country’s visa and work permit system. From understanding how to secure sponsorship as a Skilled Worker to exploring the Global Talent Visa for leading experts, this article will help you understand the most relevant routes, criteria, and practical steps for your move. Let’s delve into everything you need to know about working in data science in the UK as an international professional.
Data has become the linchpin of modern innovation. From forecasting consumer behaviour to enabling cutting-edge health research, data science underpins breakthroughs across nearly every industry. In the United Kingdom, the data science landscape is particularly robust, fuelled by a blend of academic excellence, government support, and vibrant private-sector collaborations. For jobseekers or career-changers keen to explore opportunities in this exciting field, DataScience-Jobs.co.uk offers a gateway to the latest openings, news, and resources. In this in-depth article, we’ll tour the top UK data science labs and institutes, highlight the unique research and career paths available, and outline how you can position yourself to thrive in a field that’s as dynamic as it is rewarding.
How to Find Mentors, Build Industry Connections, and Hone the Skills Needed in a Fast-Evolving Field Introduction Over the past decade, data science has grown from a niche academic discipline to a pivotal function driving decision-making in businesses of all sizes. With an array of applications—from predictive analytics and natural language processing to recommender systems and computer vision—data science offers an enticing career path for analytically minded professionals. However, as the field expands, so too does the level of competition. Employers seek not just theoretical knowledge but also real-world experience and robust problem-solving skills. That’s where shadowing and mentorship become game-changers for early-career data scientists. These hands-on learning opportunities provide an unmatched window into the workflows, tools, and soft skills you’ll need to excel in a professional environment. Whether you’re still completing your studies, fresh out of a bootcamp, or transitioning from another career, working closely with experienced data scientists can significantly shorten your learning curve and help you stand out when applying for your first full-time position. This article explores how to find mentors who align with your goals, the best ways to engage in shadowing opportunities, and practical tips for showcasing your growth as a mentee. From clarifying the nature of data science roles to leveraging online networks, you’ll discover how to position yourself as a candidate poised to solve complex challenges and drive data-driven innovation.
Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.
