Data Science

Axle Energy; Greater London; Hybrid; GBP 75,000 - 200,000; Full time

Benefits offered by this job

A meaningful slice of equity

Hybrid working

Bi-annual retreats

Dog-friendly office

Job summary

Axle Energy is seeking a full stack data scientist to own end-to-end model development from research to production. You will work with Python + React, deploy in Docker, and run on GCP, building scalable models for energy markets and grid dynamics.

You will collaborate with a small cross-functional team, engage with clients, and move ideas from notebooks into production systems while aiming for commercial impact. Hybrid, dog-friendly office in London with mostly in-office days.

Qualifications

• Experience building models from data exploration to production.
• Understanding of energy markets, grid dynamics, and trading ideas.
• Ability to ship production-grade code and robust systems.

Responsibilities

• Build, optimise, and deploy models that operate at scale.
• Develop domain understanding of energy markets and trading strategies.
• Own full lifecycle from research through production.
• Write clean, production-quality code for scalable systems.
• Turn research into real-world, productionised solutions.

Skills

Python

React

Time-series data

Client communication

Tools

Docker

GCP

Job description

About Us

At Axle Energy, we're building the infrastructure that will underpin the decarbonised energy system. Our software moves energy usage to times when electricity is cheap and green, by controlling vehicle charging, heating systems, and home batteries. We control hundreds of thousands of energy assets.

We're building in a legacy industry and moving gigawatt-hours of electrons in the real world, but we operate at lightning speed, and we're growing the team to meet customer demand.

We're proud to be supported by world-leading investors, including Energize Capital and Accel.

Read more about what we're building here.

About the role

We're hiring full stack data scientists who love to get into the nitty gritty of the domain, have an instinct for commercial value, and own the entire process from data exploration to production.

• Building, optimising, and deploying models that operate reliably at scale


• Developing a deep understanding of energy markets, grid dynamics, and trading strategies


• Taking ownership of the full lifecycle of your work, from research and prototyping through to production


• Writing clean, production-quality code and contributing to robust, scalable systems


• Working beyond notebooks, turning ideas into real-world, productionised solutions

It'd be nice if you could bring:

• Knowledge of the electricity system, specifically power trading


• Comfort speaking to clients (we're a small team and wear many hats)


• Familiarity with time-series data

Tech stack

• We build in Python + React for the frontend (less relevant for this role)


• Everything we build lives in Docker, for minimal cross-platform faff and maximal reproducibility.


• Unlimited Claude Code tokens - we are aggressive users and explorers of AI, and encourage everybody to reconsider their workflows regularly


• We deploy on GCP

What's in it for you

• A meaningful slice of equity in Axle, alongside a competitive salary, with total compensation ranging from £75k-£200k (base salary + equity).


• We operate with a deliberately flat structure and aim to keep pay equitable across the company, with a 1:1 median ratio between founder and team compensation.


• Enhanced parental leave to support you through life's meaningful moments.


• Bi-annual retreats to strengthen team connection & shared purpose.


• Hybrid working - We have a dog-friendly office around Farringdon. To maximize collaboration, we ask that you spend 2-3 days a week in the office.

We are extremely keen to build a diverse company, and we're particularly eager to hear from candidates who don't fit the traditional role stereotypes. If you're motivated by our mission, please do reach out, even if you feel you might not 'check all the boxes'.

Interview process

• Initial interview


• Take-home exercise


• Final interview (in-person)


• Offer, references, and welcome to the team!