Data Science Manager

Atana Elements
Poplar
1 month ago
Applications closed

Related Jobs

View all jobs

AI & Data Science Manager / Senior Manager

AI & Data Science Manager / Senior Manager

AI & Data Science Manager / Senior Manager

AI & Data Science Manager / Senior Manager

Gen AI & Data Science Manager — Lead AI-Driven Innovation

Applied AI Data Science Manager – Lead & Deliver

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 to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.