Data Science Manager

Atana Elements
London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Science Manager – Property Tech – London

Data Science Manager - Property Tech - London

Data Science Manager – Property Tech – London

Manager, Data Science

Data Science and Analytics Manager

Data Analytics Manager

About Atana Elements
Atana Elements is a data-driven critical mineral e xploration company looking to identify new resources around the world to help the world transition to a cleaner, greener future. The company has recently spun-out from Lilac Solutions, a leading cleantech company based out of the USA . Atana Elements is backed by some of the biggest names in cleantech including Chris Saccas Lower c arbon Capital (LCC) and Hitachi V entures .
Role Overview
In this role, you will lead Atanas growing data science division in building innovative , cutting edge tools and databases to facilitate critical mineral discovery . U nderpin ning the exploration program, you r role will be to push the deployment of machine learning models to predict resource discovery worldwide. This role offers a unique opportunity to build a new team and apply innovative solutions to uncover resources critical to the energy transition.
In this role, you will:
Lead and grow a team of data scientists to help identify and interrogate critical mineral resources

Contribute directly to the development of data science toolkits that span the mineral exploration process

Grow global datasets of geochemical, geophysical , geological and commercial data from a wide array of sources

Manage our cloud and data infrastructure , maintaining scalability as our team grows

Use effective data story-telling to communicate complex analysis to the wider team

Foster innovation through the adoption of new applications of AI /ML models

Minimum Candidate Requirements
The ideal candidate will be a motivated and driven data science manager , with the following qualifications :
Bachelors degree in Statistics, Mathematics, Data Science, Engineering, Physics, E arth Science , or a related quantitative field or equivalent practical experience

At least 7 years of experience using data science to solve complex problems

Experience with database languages such as SQL and python scripting

Strong understanding of cloud-based architecture (GCP, AWS)

Demonstrated ability to incorporate AI models into data workflows

Preferred Candidate Requirements
Experience with subsurface and geospatial datasets

Track record of leading analytical teams

Ability to work in person at Atanas technical HQ in Canary Wharf, London

Compensation
Competitive salary and benefit package

Stock options in Atana Elements

TPBN1_UKTJ

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.