At a Glance
- Tasks: Analyse data, create models, and present insights using advanced tools.
- Company: Join a dynamic team in a leading tech environment focused on innovation.
- Benefits: Enjoy competitive pay, flexible work locations, and opportunities for growth.
- Other info: This role offers a chance to work with AI/ML toolsets for cutting-edge analysis.
- Why this job: Make an impact with your skills while working in a collaborative culture.
- Qualifications: Expertise in SQL, data visualisation, and recent experience in data cleansing required.
Job title: Data Analyst
Start Date: ASAP
Contract: six months (possibility of extension)
Location: Paddington (Hybrid)
Sector: Retail
Summary
Producing an analysis is only part of the job; understanding whether it can be trusted, explained and used to support a decision is just as important.
Our Advanced Analytics team helps make better decisions by combining commercial insight, decision science and analytical engineering. We work across product squads and the wider business on trusted, high-impact analytical solutions.
As a Data Analyst, you'll contribute across the analytical process, from preparing and exploring data through to statistical modelling, experimentation, impact evaluation and communicating results. You'll work with business and product teams to investigate complex commercial questions and generate robust evidence.
This role would suit someone with strong statistical knowledge who is excited by applying it to real-world problems. You don't need extensive commercial experience, but you should bring curiosity, experience using data, exposure to advanced methods and a desire to develop in a business environment.
What you'll do
Your key accountabilities will include
- Contribute to analytical work on high-value opportunities, collaborating with experienced analysts, data scientists, product teams and business stakeholders to support better decisions.
- Help translate complex or ambiguous business questions into clear analytical objectives, success measures and structured analysis plans.
- Prepare, explore and analyse data to understand performance, identify meaningful patterns and drivers, and uncover opportunities to create measurable business value.
- Support the design and application of appropriate approaches, including statistical modelling, experimentation and quasi-experimental methods, to test hypotheses, evaluate initiatives and quantify their impact.
- Validate analytical outputs and communicate findings, uncertainty and limitations clearly, helping stakeholders understand what the evidence does and does not support.
Who you are
Your skills and experience will include
- Strong mathematical and statistical knowledge, including familiarity with probability, statistical inference, regression, experimental design and the interpretation of results.
- Exposure to advanced analytical and/or machine learning methods through study, research or practical experience, with an interest in understanding when different approaches are appropriate and what their limitations are.
- Strong analytical problem-solving skills, with the curiosity and structured thinking needed to investigate unfamiliar or ambiguous questions using imperfect real-world data.
- A clear communicator who can explain analytical approaches and findings to others, with the potential to develop this skill further when working with non-technical audiences.
- Experience using at least one data-focused programming or analytical language, such as Python, R, SQL, PySpark or similar.
Role is placed within the Advanced Analytics function, supporting the strategic Insights Engine programme.
The project aims to move retail colleagues away from consuming large volumes of reports and towards AI-enabled, prescriptive, exception-based insights that highlight where intervention is required and measure the business impact of those actions.
The key hiring requirement is an analyst with strong experimentation, evaluation and statistical analysis skills, particularly around measuring intervention effectiveness through A/B testing, quasi-experimental methods, and stakeholder-facing insight delivery.
Key Responsibilities
- Analytical discovery and opportunity identification.
- Value case assessment.
- Data preparation and ETL support.
- Feature engineering collaboration with Data Scientists.
- Testing and evaluation of interventions.
- Experiment execution and results analysis.
- Presenting outcomes to senior stakeholders.
- Influencing business decisions through analytics.
Must-Have Skills
Technical
- Statistical modelling.
- A/B testing experience.
- Experimental design understanding.
- Difference-in-differences or similar quasi-experimental techniques.
- Data analysis and interpretation.
- Ability to execute analytics frameworks designed by senior analysts.
Business
- Stakeholder communication.
- Translating analysis into actionable business recommendations.
- Working within cross-functional teams
Nice-to-Have Skills
- Exposure to Data Science environments.
- Retail analytics experience.
- Experience working alongside Product Managers, Data Scientists and Data Engineers.
- Broad analytical toolkit across multiple techniques.
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Data Analyst in London employer: Experis
As a leading employer in Northern Ireland, we offer an exciting opportunity for a Data Centre Technical Project Manager (MEP) to join our dynamic team focused on cutting-edge AI Cluster Data Centre Expansion. Our work culture promotes collaboration and innovation, providing employees with the chance to grow their skills in a mission-critical environment while enjoying competitive rates and a supportive atmosphere that values technical expertise and operational excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst in London
✨Tip Number 1
Familiarise yourself with the latest trends in data analysis, especially focusing on AI and machine learning toolsets. This will not only enhance your skills but also show us that you're proactive and up-to-date with industry standards.
✨Tip Number 2
Network with current or former Data Analysts at StudySmarter or similar companies. Engaging in conversations can provide you with insights into our work culture and expectations, which can be invaluable during interviews.
✨Tip Number 3
Prepare to discuss specific projects where you've used SQL Server and data visualisation tools. Being able to articulate your hands-on experience will demonstrate your expertise and make you stand out.
✨Tip Number 4
Brush up on your communication skills, as presenting data effectively is crucial for this role. Practising how to convey complex information clearly can set you apart from other candidates.
We think you need these skills to ace Data Analyst in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with Microsoft SQL Server, data modelling, and any relevant tools like R or Python. Use specific examples to demonstrate your skills in data visualisation and statistical analysis.
Craft a Strong Cover Letter:In your cover letter, explain why you're interested in the Data Analyst role and how your recent experience aligns with the job requirements. Mention your familiarity with AI/ML toolsets and your ability to communicate complex data insights effectively.
Showcase Relevant Projects:If you have worked on projects involving data cleansing, source to target mapping, or ETL processes, be sure to include these in your application. Highlight any specific achievements or outcomes from these projects.
Proofread Your Application:Before submitting, carefully proofread your application for any spelling or grammatical errors. A well-written application reflects your communication skills, which are crucial for this role.
How to prepare for a job interview at Experis
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Microsoft SQL Server, R/Python, and data visualisation tools. Bring examples of past projects where you successfully used these skills, as this will demonstrate your expertise and ability to handle the role's requirements.
✨Prepare for Data Modelling Questions
Since data modelling is a key part of the job, brush up on your knowledge and be ready to explain your approach to creating data models. You might be asked to walk through a specific example, so having a clear process in mind will help you stand out.
✨Communicate Clearly
Excellent communication skills are essential for this role. Practice explaining complex data concepts in simple terms, as you may need to present your findings to non-technical stakeholders. This will show that you can bridge the gap between data and decision-making.
✨Demonstrate Recent Experience
Highlight your recent experience with data cleansing and ETL processes. Be ready to discuss specific challenges you faced and how you overcame them, as well as any AI/ML toolsets you've used for data analysis. This will show that you're up-to-date with industry practices.
