Senior Data Analyst - Self-Serve Insights, ML & Remote

Senior Data Analyst - Self-Serve Insights, ML & Remote

Full-Time 45000 - 55000 £ / year (est.) Home office (partial)
Moneysupermarket Group

At a Glance

  • Tasks: Deliver high-impact analytics projects and build self-serve reports.
  • Company: Join Moneysupermarket Group, a leader in digital payments.
  • Benefits: Enjoy up to 27 holidays, pension contributions, and flexible work options.
  • Other info: Dynamic remote work environment with great career growth potential.
  • Why this job: Make a real impact by providing actionable insights across teams.
  • Qualifications: Experience with data analytics, BI tools like Tableau, and SQL required.

The predicted salary is between 45000 - 55000 £ per year.

Moneysupermarket Group is seeking a skilled data analyst to join their digital payment team in Greater London. This role involves delivering high-impact analytics projects, building self-serve reports, and collaborating with various teams to provide actionable insights.

The ideal candidate will have experience with data analytics, BI tools like Tableau, and SQL.

Enjoy benefits including up to 27 holidays, employer pension contributions, and flexible work arrangements.

Senior Data Analyst - Self-Serve Insights, ML & Remote employer: Moneysupermarket Group

Moneysupermarket Group is an excellent employer that fosters a collaborative and innovative work culture, particularly within the dynamic environment of Greater London. Employees benefit from generous holiday allowances, robust pension contributions, and flexible working arrangements, all while having the opportunity to grow their skills in data analytics and contribute to impactful projects that drive business success.

Moneysupermarket Group

Contact Details:

Moneysupermarket Group Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior Data Analyst - Self-Serve Insights, ML & Remote

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Moneysupermarket Group!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Analyst - Self-Serve Insights, ML & Remote at Moneysupermarket Group.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Moneysupermarket Group.

Apply Directly through Our Website

When you find a suitable opening like Senior Data Analyst - Self-Serve Insights, ML & Remote at Moneysupermarket Group, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Senior Data Analyst - Self-Serve Insights, ML & Remote

Problem-Solving Skills
Communication Skills
SQL
Attention to Detail
Python
Automation
Data Governance

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Moneysupermarket Group, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Moneysupermarket Group. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Moneysupermarket Group

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Moneysupermarket Group!

Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.