Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Analyst

The Benchmarking Network
Manchester
4 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

About the Role

We are looking for an experienced Data Analyst with analytical and problem-solving skills to join our team in delivering high-quality, evidence-based insights to the health and social care sector. The primary purpose of the role is to manage, review, validate, analyse and interpret a variety of datasets, with a core focus on extracting meaningful insights that inform service improvement and strategic decision-making.

The Data Analyst will be involved in collecting, processing, validating, and analysing large datasets, producing visualisations and reports, and contributing to the design of benchmarking projects and products. Working collaboratively with cross-functional teams, the Data Analyst will play a key role in delivering impactful analytics to our clients.


Job Opportunity

Data Collection and Acquisition

  • Gather data from primary (internal systems, surveys) and secondary (public datasets, APIs) sources.
  • Collaborate with IT and data engineers to access relevant data.
  • Automate data collection and reporting processes using Python, R, or SQL.

Data Cleaning and Preparation; Analytics Processes

  • Handle missing or inconsistent data.
  • Format and structure data for analysis.
  • Use tools like SQL, Excel, R, Python for wrangling.
  • Provide support for analytics processes, methods and tools to ensure maximum efficiency, accuracy, and security.
  • Create processes to validate data across several datasets.
  • Manipulate and move data in various formats and from/to different systems.
  • Ensure data stored in various systems is correct, accurate and reliable.

Data Analysis and Interpretation

  • Analyse raw data, perform exploratory data analysis (EDA) to find trends, patterns, or anomalies.
  • Use statistical techniques to test hypotheses or model relationships. Should be comfortable with analysis methods such as correlation, regression and hypothesis testing, in both parametric and non-parametric forms.
  •  Interpret trends and deliver actionable insights to clients.

Reporting and Data Visualisation

  • Provide support to operational work – regular reporting projects. Maintain tools and dashboards and update data regularly.
  • Build recurring reports and automate them when possible.
  • Design and create visual representations of data: dashboards, charts, and reports using tools like Power BI.
  • Present data in a clear, understandable way to clients.

Reporting, Storytelling with Data, and Communication

  • Translate technical findings into data insights. Explain analytical findings in a compelling, digestible format to non-technical audiences.
  • Bridge the gap between data and decision-makers.
  • Communicate results through presentations, reports, and briefings.
  • Ensure developed products are delivered to set timescales.

Cross-team Collaboration

  • Work with all teams: programme teams, product team, dev hub, network development and others.
  • Collaborate with and understand programme team’s requirements.


Essential Skills

  • University Degree or equivalent.
  • At least 2 years’ industry experience.
  • Strong background in technology, mathematics, or another related field
  • Knowledge of mathematical concepts such as correlation analysis, hypothesis testing, and regression, and being able to carry out this analysis in both parametric and non-parametric forms
  • Excellent IT skills including fluency with all Microsoft packages, particularly Excel, Word, PowerPoint, PowerBi.
  • Experience in R programming language, including RShiny and/o RMarkdown.
  • Knowledge and proficiency in working with database systems (SQL/PostgreSQL) and writing performant SQL queries.
  • Experience in data manipulation, data cleaning and preparation.
  • Experience in data analysis and interpretation, data modelling.
  • Experience in data visualisation.
  • Ability to meet project deadlines.
  • Strong track record of problem-solving ability and troubleshooting skills.
  • Evidence of excellent communication and interpersonal skills. Clearly communicate technical concepts to both technical and non-technical audiences.
  • Self-motivated and capable of working independently, while also enjoying collaboration and contributing as a team player.
  • Adaptability and willingness to learn new technologies.
  • Willingness to work flexibly and adaptably.


About Company

We are the official research support team for the NHS Benchmarking Network, working with over 300 healthcare organisations across the UK to identify opportunities for service development and quality improvement.

We also serve as the primary source of benchmarking data for the NHS England Workforce, Training and Education team, providing in-depth analysis across national mental health services. Our unique and comprehensive datasets support strategic decision-making and resource allocation across the UK healthcare system.

In addition, we are proud to facilitate two major national audits: the National Audit for Care at the End of Life (NACEL) and the Cardiovascular Disease Prevention Audit (CVDPREVENT).


Benefits

  • Annual bonus scheme (subject to personal and business performance)
  • 25 days holiday increasing to 28 after 18 months service, plus statutory bank holidays
  • Flexible 8-hour shift around 6 core working hours
  • Contributory pension
  • Wellbeing programme, including EAP
  • Life Assurance

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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.