At a Glance
- Tasks: Lead the development of machine learning models to combat fraud and protect customers.
- Company: Join Wise, a leader in financial technology focused on security and innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Be part of a dynamic team with a mission to enhance customer safety.
- Why this job: Make a real difference in preventing fraud while working with cutting-edge technology.
- Qualifications: Experience in deploying models, strong Python skills, and a collaborative mindset.
The predicted salary is between 70000 - 90000 € per year.
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.
Our vision
- Build a globally scalable fraud prevention and detection engine to maintain Wise as a secure environment for our legitimate customers.
- Utilise machine learning techniques to identify potential risks associated with customer activity.
- Foster a strong partnership between our fraud investigators and the product team to develop solutions that leverage the expertise of fraud prevention specialists.
- Not only meet the requirements set by regulators and auditors but also surpass their expectations.
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.
How you’ll be contributing
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 space.
Key Responsibilities
- Model Maintenance and Improvement
- Maintain and optimise existing risk models to ensure their accuracy and reliability.
- Continuously monitor model performance and implement improvements based on feedback and testing.
- Innovate and Develop
- Lead the development and deployment of machine learning models, features and help deploy intelligence to production.
- Data Analysis & Intelligence Creation
- 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.
- Collaboration & Communication
- Work closely with the Fraud Risk Team to understand business processes and risk factors.
- Communicate complex data findings and insights effectively to non-technical stakeholders.
- Risk Reduction Initiatives
- 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.
- Documentation & Reporting
- Document the development and maintenance processes for models and features.
- Prepare and present detailed reports and dashboards that reflect risk assessment outcomes and model performance.
Qualifications
A bit about you
- 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.
- Demonstrable experience collaborating with engineering on services.
- Experience with statistical analysis and good presentation skills to drive insight into action.
- A strong product mindset with the ability to work independently in a cross‑functional and cross‑team environment.
- Good communication skills and ability to get the point across to non‑technical individuals.
- Strong problem solving skills with the ability to help refine problem statements and figure out how to solve them.
Some extra skills that are great (but not essential)
- Experience on working with non supervised algorithms.
- Prior experience in the fraud domain and a strong understanding of fraud detection techniques.
Lead Data Scientist - Fraud Prevention in London employer: Dangote Industries Limited
At Wise, we pride ourselves on being an exceptional employer, particularly for our Lead Data Scientist role within the Fraud Risk Team. Our collaborative work culture fosters innovation and continuous learning, allowing you to leverage cutting-edge machine learning techniques while contributing to a mission that safeguards our customers. With ample opportunities for professional growth and a commitment to exceeding regulatory expectations, Wise offers a rewarding environment where your expertise can make a significant impact in fraud prevention.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist - Fraud Prevention in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Wise. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a project that highlights your machine learning prowess and fraud detection insights. This is your chance to shine!
✨Tip Number 3
Practice makes perfect! Get ready for interviews by doing mock sessions with friends or using online platforms. The more you practice, the more confident you'll feel.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take that extra step.
We think you need these skills to ace Lead Data Scientist - Fraud Prevention in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Lead Data Scientist role. Highlight your experience with machine learning, data analysis, and any relevant projects that showcase your skills in fraud prevention. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about fraud prevention and how your background aligns with our goals at Wise. Let us know what excites you about the role and how you can help us build a secure environment for our customers.
Showcase Your Collaboration Skills:Since this role involves working closely with the Fraud Risk Team and other departments, make sure to highlight your collaboration experiences. Share examples of how you've effectively communicated complex data findings to non-technical stakeholders in the past.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and ensure it gets the attention it deserves. Don’t miss out on the opportunity to join our team!
How to prepare for a job interview at Dangote Industries Limited
✨Know Your Models Inside Out
Make sure you can discuss the machine learning models you've worked on in detail. Be prepared to explain your approach to model maintenance, optimisation, and how you've implemented improvements based on performance feedback.
✨Showcase Your Data Analysis Skills
Bring examples of your data analysis work that highlight trends, patterns, and anomalies. Be ready to discuss how your insights have informed strategies in previous roles, especially in risk mitigation.
✨Communicate Like a Pro
Practice explaining complex data findings in simple terms. Since you'll be working with non-technical stakeholders, demonstrating your ability to communicate effectively is key to showing you can bridge the gap between data science and business needs.
✨Collaborate and Contribute
Highlight your experience working in cross-functional teams. Discuss how you've collaborated with engineers and product teams to develop solutions, and be ready to share specific examples of successful partnerships in your past roles.
