Remote Senior Data Scientist & Consulting Lead

Intuita
Newbury
1 month ago
Applications closed

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A fast-growing consultancy is looking for a driven Data Scientist to join their Newbury team. You will play a key role in developing data science solutions and helping clients drive commercial value. We're seeking individuals with experience in Python or R, strong analytical skills, and the ability to manage the full data science lifecycle. This position offers flexible remote working, a supportive team environment, and fantastic opportunities for personal development and growth.
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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)

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The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

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