At a Glance
- Tasks: Own and enhance our ML-driven revenue growth models while collaborating with finance and operations teams.
- Company: Join Checkout.com, a leading fintech powering payments for global brands like eBay and Spotify.
- Benefits: Enjoy a flexible hybrid work model, competitive salary, and opportunities for personal growth.
- Other info: Be part of a supportive team culture that values diversity and encourages personal development.
- Why this job: Make a real impact in the fintech space and advance your career with cutting-edge technology.
- Qualifications: Proficient in SQL and Python, with experience in predictive modelling and cross-functional collaboration.
The predicted salary is between 60000 - 75000 £ per year.
We’re Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day. We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.
As a Commercial Data Scientist at Checkout.com, you will be the primary operator of our Growth Prediction Engine. Your focus will be to maintain and evolve the models that underpin our commercial strategy, working closely with Strategic Finance and Revenue Operations.
The growth forecasting framework is an established roadmap. You will own the execution of these models while working in a shared-delivery model where tasks are distributed across the wider Data Science team.
How You’ll Make an Impact
- Advance our ML-Driven Forecasting: Own, maintain, and continuously enhance our ML-driven revenue growth models. You will be responsible for pushing these models to the next level and proactively expanding their use cases beyond the commercial domain.
- Drive Strategic Financial Modeling: Partner with Strategic Finance to build robust projections and simulations based on our forecast model outputs, approaching this with the mindset of building scalable, automated tools rather than just ad-hoc reports.
- Unlock Commercial Data Science Applications: Work closely with the Revenue Operations team to identify, unlock, and deploy other high-impact commercial data science applications that increase the quality and utilization of our data.
- Champion Best Practices: Lead by example within your team and the broader data community by applying best practices in analytics and machine learning, from data collection to deployment and analysis.
What We’re Looking For
- Strong proficiency in SQL and Python with experience building and maintaining predictive models.
- Prior experience as a Data Scientist in a commercial set-up, delivering predictive models/timeseries models to aid sale/demand forecasting.
- Experience working with cross-functional partners (Finance, Product, and Ops) to deliver actionable insights.
- Ability to explain technical data concepts to non-technical stakeholders clearly and concisely.
- Prior experience in a commercial, fintech, or high-growth environment is preferred.
Additional Information
We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one. Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands.
We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.
It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.
We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Commercial Data Scientist employer: Checkout.com
Checkout.com is an exceptional employer that champions a flexible hybrid working model, allowing employees to balance their professional and personal lives effectively. With a strong emphasis on growth and collaboration, the company provides ample opportunities for career development while working alongside talented teams in the dynamic financial services sector in London. Joining Checkout.com means being part of a forward-thinking organisation that values compliance and innovation in payments and product regulation.
StudySmarter Expert Advice🤫
We think this is how you could land Commercial 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 Checkout.com!
✨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 Commercial Data Scientist at Checkout.com.
✨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 Checkout.com.
✨Apply Directly through Our Website
When you find a suitable opening like Commercial Data Scientist at Checkout.com, 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 Commercial 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 Checkout.com, 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 Checkout.com. 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 Checkout.com
✨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 Checkout.com!
✨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.
