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Group Director, Business Intelligence & Customer Master (Basé à London)
Data Architect (Basé à London)
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Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions
Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.
Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained
Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.
Data Science Jobs in the Public Sector: Exploring Opportunities Across GDS, NHS, MOD, and More
Data science has emerged as one of the most influential fields in the 21st century, transforming how organisations make decisions, improve services, and solve complex problems. Nowhere is this impact more visible than in the UK public sector. From the Government Digital Service (GDS) to the National Health Service (NHS) and the Ministry of Defence (MOD), government departments and agencies handle vast amounts of data daily to support the well-being and security of citizens. For data enthusiasts looking to make a meaningful contribution, data science jobs in the public sector can offer rewarding roles that blend innovation, large-scale impact, and societal benefit. In this comprehensive guide, we’ll explore why data science is so pivotal to government, the roles you might find, the skills needed, salary expectations, and tips on how to succeed in a public sector data science career.