At a Glance
- Tasks: Lead the development of machine learning models to combat fraud and protect customers.
- Company: Join Wise, a global tech company revolutionising money management.
- Benefits: Competitive salary, inclusive culture, and opportunities for career growth.
- Other info: Collaborative environment with a focus on innovation and personal development.
- Why this job: Make a real impact in fraud prevention while working with cutting-edge technology.
- Qualifications: Strong Python skills, experience in fraud detection, and data analysis expertise.
The predicted salary is between 80000 - 100000 £ 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. The Fraud team at Wise is dedicated to safeguarding our platform against financial crime and ensuring the protection of our legitimate customers. Leveraging cutting-edge machine learning, real-time transaction monitoring, and data analysis, our team is responsible for developing and enhancing fraud detection systems. Software engineers, data analysts, and data scientists collaborate on a daily basis to continuously improve our systems and provide support to our fraud investigation team.
We are looking for someone who will help maintain our existing machine learning algorithms, while helping to make them better and develop new intelligence to stop fraudsters. We are seeking a highly motivated Lead Data Scientist to join our Fraud Risk Team. In this role, you will level up the intelligence and maintain and refine existing models, develop new features, and create new intelligence to reduce the impact on good customers. You will work closely with the Fraud Risk Team to support the effective management and mitigation of risks associated with our receiving processes. Further, you will help grow our data science team in this space.
- Maintain and optimise existing risk models to ensure their accuracy and reliability.
- Continuously monitor model performance and implement improvements based on feedback and testing.
- Lead the development and deployment of machine learning models, features and help deploy intelligence to production.
- Conduct thorough data analysis to identify trends, patterns, and anomalies that can aid in risk mitigation.
- Develop actionable intelligence and insights to inform the Fraud Risk Team's strategies.
- Communicate complex data findings and insights effectively to non-technical stakeholders.
- Identify opportunities to reduce the impact of risks on good customers through data-driven strategies and interventions.
- Develop and test strategies to balance risk mitigation with customer satisfaction.
- Prepare and present detailed reports and dashboards that reflect risk assessment outcomes and model performance.
Proven track record of deploying models from scratch, including data preprocessing, feature engineering, model selection, evaluation, and monitoring. Strong Python knowledge. Ability to read through code, especially Java. Experience with statistical analysis and good presentation skills to drive insight into action; strong problem-solving skills with the ability to help refine problem statements and figure out how to solve them. Prior experience in the fraud domain and a strong understanding of fraud detection techniques.
We're people building money without borders — without judgement or prejudice, too. 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.
Lead Data Scientist in Westminster employer: Wise
Wise is an exceptional employer that prioritises the well-being and professional growth of its employees. With a dynamic work culture that fosters innovation and collaboration, team members are encouraged to develop their skills while contributing to meaningful resilience strategies on a global scale. Located in a vibrant city, Wise offers competitive benefits and unique opportunities for career advancement, making it an ideal place for those seeking a rewarding and impactful career.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist in Westminster
✨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 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 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 in Westminster
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.
