At a Glance
- Tasks: Develop AI and machine learning solutions to enhance customer experiences and pricing intelligence.
- Company: Join Compare the Market, a purpose-driven tech company making financial decisions easier for millions.
- Benefits: Inclusive culture, career growth opportunities, and a chance to make a real impact.
- Other info: Collaborative atmosphere with opportunities for peer coaching and knowledge sharing.
- Why this job: Be part of a dynamic team that values innovation and accountability in a fast-paced environment.
- Qualifications: Experience in machine learning, proficient in Python and SQL, strong problem-solving skills.
The predicted salary is between 63000 - 77000 £ per year.
Join Compare the Market and help to make financial decision making a breeze for millions. At Compare the Market, we’re a purpose-driven business powered by tech and AI. We’re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.
As a Data Scientist at Compare the Market, you’ll play a key role in developing AI and machine learning solutions that improve customer journeys, pricing intelligence, and product personalisation. Working cross functionally alongside engineers, analysts, and product managers contributing to real-world applications of machine learning that impact millions.
Some of the great things you’ll be doing:
- AI Solution Delivery
- Contribute to the development of machine learning solutions from design through to deployment
- Build and evaluate predictive models using Python and SQL
- Translate business challenges into structured data science problems
- Applied Science & Experimentation
- Conduct statistical analysis and feature engineering to generate actionable insights
- Support A/B test design, evaluation, and interpretation with rigour and clarity
- Collaborate with stakeholders to ensure data science solutions are well aligned to product and business needs
- Platform & Practices
- Work with ML Engineers to support deployment, monitoring, and performance tracking of your models
- Contribute to team standards on reproducibility, documentation, and model evaluation
- Apply responsible AI practices to ensure fairness and transparency
- Take part in peer reviews, knowledge sharing, and internal learning sessions
- Support and coach junior team members and peers, contributing to their growth
- Play an active role in shaping our data science culture and community
What we’d like to see from you:
- Must Have
- Experience delivering machine learning or advanced analytics solutions with measurable impact
- Proficient in Python and SQL, with practical experience in modelling, feature engineering, and evaluation
- Strong problem-solving skills and ability to break down complex problems into data-driven approaches
- Comfortable working collaboratively in cross-functional teams
- Clear communicator with a passion for sharing insights and learning from others
- A background in a quantitative discipline (e.g. Computer Science, Mathematics, Statistics, Engineering) or equivalent hands-on experience in applied machine learning
- Nice to Have
- Experience working in regulated sectors such as insurance, banking, or financial services
- Familiarity with tools like MLflow, model registries, or experimentation platforms
- Understanding of basic CI/CD concepts or exposure to production ML workflows
- Awareness of responsible AI principles, such as fairness or explainability in models
Why Compare the Market? We’re a business built for pace and performance. Here, you’ll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress. We believe diverse teams make better decisions, and we’re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive. If you’re ready to stretch yourself, raise the bar, and grow with a team that’s serious about performance, innovation, and purpose, we’d love to hear from you.
Data Scientist employer: Women in Data®
Women in Data® is an exceptional employer that champions diversity and innovation within the tech industry. With a strong commitment to employee growth, you will benefit from generous annual leave, ongoing development opportunities, and a collaborative work culture that values mentorship and technical excellence. Join us in a role that not only enhances your career but also contributes to meaningful initiatives in data and machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist
✨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 Women in Data®!
✨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 Data Scientist at Women in Data®.
✨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 Women in Data®.
✨Apply Directly through Our Website
When you find a suitable opening like Data Scientist at Women in Data®, 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 Data Scientist
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 Women in Data®, 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 Women in Data®. 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 Women in Data®
✨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 Women in Data®!
✨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.
