At a Glance
- Tasks: Transform raw bank data into actionable insights for fraud prevention and underwriting.
- Company: Join a leading financial tech company focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with excellent career advancement opportunities.
- Why this job: Make a real impact in the world of open banking with cutting-edge technology.
- Qualifications: Experience in machine learning, Python, SQL, and AWS environments required.
The predicted salary is between 81425 - 116438 £ per year.
As a Senior Data Scientist within our Open Banking team, you will build the analytical foundation that turns raw bank account data into reliable, actionable insight for fraud prevention and underwriting decisions. You will engage in feature engineering across incoming and outgoing cash flows to model affordability, and develop machine learning models that estimate probability of default and produce fraud scorecards. You will own the full lifecycle of these models, from raw data through to production deployment, working closely with engineering to operationalize your work at scale. You will also help monitor model performance, impact, and acceptance rates through dashboards you help build, and you will partner closely with the Credit Modeling and Fraud teams to align on shared goals and infrastructure.
Who you are
- Experience building and deploying machine learning models end to end, from raw data to production
- Strong feature engineering skills, ideally with transactional or financial data
- Proficient in Python and SQL, with experience working in AWS environments
- Solid understanding of classification modeling techniques for risk or fraud use cases
- Comfortable partnering closely with engineering teams to bring models into production
- Strong communication skills, with the ability to work cross-functionally with Credit Modeling and Fraud teams
- A curious, ownership-driven mindset, comfortable building infrastructure and processes from scratch
Awesome to have
- Experience with open banking data or affordability modeling
- Familiarity with building dashboards for model monitoring, ideally with tools like Databricks
- Experience with monitoring and observability tools such as Datadog
- Background in underwriting, credit risk, or fraud detection use cases
Please include a CV in English.
Senior/Lead Data Scientist - Open Banking in London employer: PLP Group
CellPoint Digital is an exceptional employer that champions innovation and collaboration in the heart of London. With a strong focus on employee growth, we offer competitive salaries, a dynamic work culture, and opportunities to lead transformative projects in cloud technology. Join us to be part of a forward-thinking team that values automation, security, and developer experience.
StudySmarter Expert Advice🤫
We think this is how you could land Senior/Lead Data Scientist - Open Banking in London
✨Get Involved in Data Science Meetups
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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 PLP Group.
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We think you need these skills to ace Senior/Lead Data Scientist - Open Banking in 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 PLP Group, 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 PLP Group. 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 PLP Group
✨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 PLP Group!
✨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.
