Data Science Engineer

Lorien
London
3 months ago
Applications closed

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Senior Data Consultant / Scientist - KPI Dashboarding (Contract)?? Hybrid (Remote + 1 day/week in Heathrow)?? £800/day (Inside IR35)?? Start Date: Week commencing 17th November 2025?? Duration: 8 weeks________________________________________About the RoleA world leading premium airline is launching a new engineering workstream focused on building a KPI dashboard to support transformation initiatives. We're seeking a Senior Data Consultant / Scientist for a short-term contract to lead the dashboard development and embed it into an existing programme.________________________________________Key Responsibilities* Design and build KPI dashboards using Power BI or Tableau* Integrate backend data sources (Excel and others) into visual outputs* Define KPIs, identify data sources, scope dashboard requirements, and iterate with stakeholders* Ensure data quality through modelling, cleaning, and verification* Collaborate with engineering teams to understand operational metrics and reporting needs________________________________________Required Skills* Expert-level proficiency in SQL, Power BI, and Tableau* Strong experience in data visualisation and dashboarding* Solid understanding of data modelling, data cleaning, and data verification* Ability to work independently and embed into fast-moving programmes* Comfortable accessing and transforming data into actionable insights________________________________________Preferred Background* Experience in engineeri...

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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.

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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.

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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.

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