At a Glance
- Tasks: Transform data into actionable insights and shape smarter collections strategies.
- Company: Dynamic Credit Risk function focused on innovative analytics.
- Benefits: Hybrid working, competitive salary up to £54,000, and exciting project opportunities.
- Other info: Join a collaborative team with opportunities for career growth.
- Why this job: Make a real impact using data to drive decisions in credit risk.
- Qualifications: Proficient in SQL and Python, with experience in credit risk analytics.
The predicted salary is between 54000 - 54000 £ per year.
Are you ready to turn data into action and make a tangible impact? We’re looking for a skilled and driven Revenue & Debt Data Scientist to join a dynamic Credit Risk function. This is a unique opportunity to work at the forefront of credit risk analytics, helping shape smarter collections strategies, reduce bad debt, and improve outcomes for customers.
What you’ll do:
- Conduct root cause analysis of debt accumulation trends.
- Build and refine predictive models for credit risk and debt recovery.
- Develop and maintain SQL-based reporting solutions to drive actionable insights.
- Collaborate across teams to align data governance, infrastructure, and reporting needs.
- Embed analytics into strategic decision-making and champion data-driven thinking.
What we’re looking for:
- Proficient in SQL for querying, joining, and transforming large datasets.
- Experienced in data cleansing, validation, and predictive modelling.
- Strong in Python for statistical analysis and able to communicate insights to non-technical stakeholders.
- Proven experience in credit risk analytics, debt management, or financial modelling.
- Comfortable working cross-functionally and translating data into actionable strategy.
- Familiarity with cloud platforms such as Azure, AWS, or Google Cloud.
- Degree (or equivalent) in Data Science, Mathematics, Statistics, or a related field.
Desirable:
- Experience migrating from traditional databases to data lake architectures.
- Background in Financial Services or other regulated industries.
- Exposure to SAP/DM9 environments.
- Knowledge of machine learning techniques relevant to credit risk.
What we offer:
- Hybrid working flexibility (South West).
- Competitive salary of up to £54,000 per annum + benefits.
- Opportunities to work on high-impact projects that shape strategy and operations.
If you’re passionate about using data to drive decisions and make a difference, we’d love to hear from you.
Data Scientist in England employer: Cactus Search
Join a forward-thinking company that values innovation and collaboration, where as a Data Scientist in the South West, you will have the opportunity to work on high-impact projects that directly influence credit risk strategies. With a competitive salary and hybrid working flexibility, we foster a supportive work culture that prioritises employee growth and development, ensuring you can thrive in your career while making a meaningful impact on our customers' financial outcomes.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist in England
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow data enthusiasts. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive models and SQL reporting solutions. This is your chance to demonstrate how you turn data into actionable insights, making you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and being ready to discuss your experience in credit risk analytics. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with non-technical stakeholders.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team and making an impact.
We think you need these skills to ace Data Scientist in England
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of a Data Scientist. Highlight your experience with SQL, Python, and any relevant projects in credit risk analytics. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about data-driven decision-making and how you can contribute to our Credit Risk function. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects:If you've worked on any projects related to predictive modelling or debt management, make sure to mention them. We’re keen to see real examples of your work and how you’ve used data to drive insights and strategies.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Cactus Search
✨Know Your Data Inside Out
Before the interview, dive deep into your past projects involving data cleansing, predictive modelling, and SQL. Be ready to discuss specific examples where you turned data into actionable insights, especially in credit risk analytics.
✨Brush Up on Your Technical Skills
Make sure you're comfortable with SQL and Python, as these are crucial for the role. Practise writing queries and performing statistical analysis so you can confidently demonstrate your skills during technical questions.
✨Prepare for Scenario-Based Questions
Expect questions that ask how you would handle specific situations, like a sudden increase in debt accumulation trends. Think about your approach to root cause analysis and be ready to explain your thought process clearly.
✨Show Your Collaborative Spirit
Since the role involves working cross-functionally, prepare examples of how you've successfully collaborated with other teams. Highlight your ability to communicate complex data insights to non-technical stakeholders, making it clear that you can champion data-driven decision-making.
