At a Glance
- Tasks: Develop predictive models to drive marketing growth and customer insights.
- Company: Wise, a global tech company revolutionising money management.
- Benefits: Competitive salary, inclusive culture, and opportunities for career progression.
- Other info: Join a diverse team committed to innovation and inclusivity.
- Why this job: Make a real impact on how millions manage their money globally.
- Qualifications: Experience in data science and strong analytical skills.
The predicted salary is between 60000 - 68205 £ per year.
Wise is a global technology company, building the best way to move and manage the world’s money. As part of our team, you will be helping us create an entirely new network for the world's money. Your mission is to help make people aware of Wise as a solution for cross-border money needs.
Here’s how you’ll be contributing to Marketing:
- You will help the Marketing tribe find the biggest opportunities for growth.
- You will help us understand in what growth activity to invest (Marketing Mix Models) and how to reduce the uncertainty on marketing measurement.
- You will do this by developing predictive models to calculate Customer Lifetime Value (LTV), aiding in the prioritisation of marketing efforts and resource allocation.
- You will model customer behaviour data and product usage so we understand which audiences to target and how.
- You will work closely with Data Analysts and you will help them understand and use models that you build (LTV or MMM models).
- Your average day will include building new models, maintaining models used by everyone in the marketing tribe, evaluating new ideas and communicating what models can tell us about how and why we grow.
Have a direct impact - You will closely partner with every marketing team within both Organic and Paid Acquisition and help millions of people and businesses to learn about how Wise can help them.
Be part of a diverse team - You will work in a team of Data scientists, Analysts and Marketeers. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist - Marketing (London)
✨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 Wise!
✨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 Lead Data Scientist - Marketing (London) at Wise.
✨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 Wise.
✨Apply Directly through Our Website
When you find a suitable opening like Lead Data Scientist - Marketing (London) at Wise, 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 Lead Data Scientist - Marketing (London)
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 Wise, 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 Wise. 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 Wise
✨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 Wise!
✨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.
