Data Scientist: Pipelines, ML & Insights for Energy Tech

Evera Recruitment Ltd; Cambridgeshire and Peterborough; On-site; GBP 55,000 - 75,000; Full time

Job summary

Evera Recruitment Ltd is seeking a Data Scientist to help build the data foundations that support next‑generation energy storage projects. You will convert raw experimental data into meaningful insights, scalable analytics and clear visualisations, enabling faster, more confident decisions across product development.

You will develop tools and workflows to automate extraction and processing, design data pipelines and dashboards, apply statistics and machine learning, and present findings to both

Qualifications

• Degree in Computer Science, Engineering, Physical Sciences or related quantitative discipline.
• Strong programming skills focused on data analysis and workflow development.

Responsibilities

• Develop tools and workflows to automate extraction, processing and analysis of experimental data.
• Build and maintain data pipelines, databases and analytical frameworks.
• Generate meaningful features, visualisations and reports from complex datasets.
• Apply statistical analysis and machine learning techniques to support product development decisions.
• Present insights to both technical and non-technical stakeholders.
• Develop scripts, tools and automation solutions to improve business efficiency.
• Identify opportunities to enhance existing data methodologies and accelerate development cycles.

Skills

Python

Data analysis

Data visualization

Machine Learning

Git

Workflow

Education

Quantitative degree

Tools

Git

SQL

Job description

Evera Recruitment Ltd is seeking a Data Scientist to help build the data foundations that support next‑generation energy storage projects. You will convert raw experimental data into meaningful insights, scalable analytics and clear visualisations, enabling faster, more confident decisions across product development.

You will develop tools and workflows to automate extraction and processing, design data pipelines and dashboards, apply statistics and machine learning, and present findings to both