Data Scientist

AWTG Ltd; Birmingham; On-site; GBP 40,000 - 70,000; Full time

Job summary

AWTG Ltd in Birmingham is seeking a Data Scientist to apply data science, statistics and machine learning to generate actionable insights for clients. You will collaborate with multidisciplinary teams across data, AI, software engineering, product, QA and delivery to turn analyses into practical outcomes.

The role focuses on exploring data, building models and communicating findings clearly, with emphasis on ethics, privacy and security throughout the data science life cycle.

Qualifications

• Experience applying data science methods to real-world problems.
• Proficient in Python or R for analysis and modelling.
• Understanding of machine learning, statistical testing, model validation and performance metrics.
• Ability to prepare, clean and transform data for analysis and modelling.
• Ability to communicate technical findings and visualisations to non-technical audiences.
• Awareness of data ethics, privacy, bias, model limitations and responsible use of data.
• Experience with NLP, time series, optimisation, predictive analytics or simulation (Desirable).
• Experience with cloud data platforms, notebooks, Git, APIs or data pipelines (Desirable).
• Experience delivering data science outputs within Agile or multidisciplinary teams (Desirable).

Responsibilities

• Explore, prepare, analyse and visualise data to identify patterns, trends and opportunities.
• Develop data science outputs such as models, reports, dashboards, forecasts or decision-support tools.
• Apply appropriate statistical, machine learning or analytical techniques to solve business or user problems.
• Work with data engineers, analysts, developers and product teams to deliver usable data science solutions.
• Document methods, assumptions, limitations and validation results clearly and responsibly.
• Consider data ethics, privacy and security throughout the data science life cycle.

Skills

Data science

Statistics

Python

R

Machine learning

Data visualization

Data ethics

Tools

Git

Jupyter notebooks

APIs

Cloud data platforms

Job description

Role overview The Data Scientist will use data science, statistics and machine learning techniques to generate insight and develop practical solutions for AWTG and its clients. The role works with multidisciplinary teams to explore data, build models and communicate findings clearly. You will work with multidisciplinary teams across data, AI, software engineering, product, QA and delivery to create practical outcomes for clients and end users.

Key responsibilities

• Explore, prepare, analyse and visualise data to identify patterns, trends and opportunities.
• Develop data science outputs such as models, reports, dashboards, forecasts or decision-support tools.
• Apply appropriate statistical, machine learning or analytical techniques to solve business or user problems.
• Work with data engineers, analysts, developers and product teams to deliver usable data science solutions.
• Document methods, assumptions, limitations and validation results clearly and responsibly.
• Consider data ethics, privacy and security throughout the data science life cycle.

Essential skills and experience

• Experience applying data science or statistical methods to real-world problems.
• Good knowledge of Python, R or similar programming languages for analysis and modelling.
• Understanding of machine learning, statistical testing, model validation and performance metrics.
• Ability to prepare, clean and transform data for analysis and modelling.
• Ability to communicate technical findings and visualisations to non-technical audiences.
• Awareness of data ethics, privacy, bias, model limitations and responsible use of data.

Desirable skills and experience

• Experience with NLP, time series, optimisation, predictive analytics or simulation.
• Experience with cloud data platforms, notebooks, Git, APIs or data pipelines.
• Experience delivering data science outputs within Agile or multidisciplinary teams.

What success looks like

• Data science outputs are valid, explainable and aligned to business needs.
• Models and analysis are documented, tested and communicated clearly.
• Insights help teams improve services, operations or decision-making.