Data Analyst/Scientist

Method Resourcing Ltd; West of England; Hybride; GBP 55 000 - 75 000; Plein temps

Résumé du poste

Method Resourcing Ltd partners with a leading engineering consultancy to hire a Data Analyst/Scientist to join their expanding team. This role covers the full analytics lifecycle, from defining stakeholder questions to exploring data, selecting methods, and presenting actionable insights.

You’ll apply Python, SQL and data visualisation tools to build models and dashboards, collaborate with engineers and product teams, and share reproducible approaches across the business.

Qualifications

• Strong analytical thinking and ability to turn questions into analytical problems.
• Advanced Python skills for data manipulation, analysis, and data science tasks.
• Proficient in SQL with good software engineering practices (version control, testing, etc.).
• Experience with data visualization tools (Power BI, Streamlit).
• Experience applying ML/AI techniques to practical problems.

Responsabilités

• Partner with engineers and stakeholders to translate questions into analytical problems.
• Design and deliver data-driven solutions addressing business challenges.
• Use SQL, Python and analytical tools to explore data and produce actionable insights.
• Apply statistical, forecasting, ML and AI techniques where value is added.
• Collaborate with product and software teams to convert analyses into reusable tools and data products.
• Write reproducible, maintainable code with version control and testing.

Connaissances

Analytical thinking

Python

SQL

Data visualisation

Communication

Outils

Power BI

Streamlit

Pandas

NumPy

Scikit-learn

Description du poste

Method Resourcing are delighted to partner with a leading, multi-disciplinary engineering consultancy looking to hire a Data Analyst/Scientist to join their team as they continue to grow.

This is a wide-ranging analytical role that spans the full lifecycle of a problem. You’ll start by understanding what stakeholders need, then explore and assess the available data, choose the right analytical approach, and communicate your findings clearly. Where it makes sense, you’ll also build statistical or machine-learning models, or develop client-ready insights and tools.

It suits someone who thrives on variety and has a sharp eye for matching the right solution to the problem at hand. Some projects may centre on exploratory analysis, a dashboard, or a set of well-defined metrics. Others may call for forecasting, statistical modelling, machine learning, or building and working with AI.

Responsibilities:

• Partner with engineers, consultants, product teams and other stakeholders to understand their needs and turn open-ended or unclear questions into clearly defined analytical problems.
• Design and deliver data-driven solutions that address real business challenges across the organisation.
• Use SQL, Python and other analytical tools to explore datasets, uncover trends and relationships, test hypotheses and produce insights that can be acted on.
• Select and apply suitable statistical, forecasting, machine learning and AI techniques where they genuinely add value.
• Work with product and software engineering teams to convert analytical work into reusable tools, workflows and data products where it makes sense.
• Write reproducible, maintainable code in line with best practice, including version control, testing and peer review.
• Help build analytical and data science capability across the business by sharing methods, reusable approaches and good practice.

About you:

• Track record of using data and analysis to solve real-world business, engineering or operational challenges.
• Strong analytical thinking, with the ability to turn a loosely defined question into a clear, well-chosen analytical approach.
• Able to work autonomously and take ownership of project deliverables.
• Advanced Python skills for data manipulation, analysis, visualisation and data science (e.g., Pandas, NumPy, Scikit-learn).
• Proficient in querying languages such as SQL, with a solid grounding in software engineering best practice (version control, unit testing, code review, etc.).
• Significant experience with data visualisation tools (e.g., Power BI, Streamlit).
• Experience applying machine learning and AI, including generative AI, to practical problems where it delivers clear value.
• Working knowledge of statistical concepts and how to apply them in data analysis.
• Excellent communication skills, with the ability to engage with and present findings to both technical and non-technical audiences.
• Awareness of data governance, privacy, responsible AI and other relevant standards.

Bristol/London-based; 1 day every fortnight in the office.