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- Tasks: Transform data into actionable insights and lead predictive modelling for credit risk.
- Company: Join a dynamic team at the forefront of credit risk analytics.
- Benefits: Hybrid working, competitive salary up to £54,000, and exciting project opportunities.
- Why this job: Make a real impact on financial resilience and customer outcomes through data.
- Qualifications: Proficient in SQL and Python, with experience in credit risk analytics.
- Other info: Collaborative environment with opportunities for professional growth.
The predicted salary is between 43200 - 57600 £ 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. In this pivotal role, you’ll lead deep-dive analysis into customer portfolio trends, build predictive models, and support the transition to an enterprise data lake. Your insights will directly influence operational improvements, strategic decisions, and long-term financial resilience.
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 Gloucester employer: Cactus Search
Contact Detail:
Cactus Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist in Gloucester
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, predictive models, and any SQL reports you've built. This is your chance to demonstrate your expertise and make a lasting impression on hiring managers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and practising common data science questions. Be ready to discuss your experience with credit risk analytics and how you've used data to drive decisions in past roles.
✨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 in the world of data.
We think you need these skills to ace Data Scientist in Gloucester
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 that showcase your skills in credit risk analytics and predictive modelling.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you’re passionate about data and how you can make a tangible impact in our Credit Risk function. Share specific examples of how you've turned data into actionable insights in previous roles.
Showcase Your Technical Skills: Don’t forget to mention your proficiency in SQL and Python! We want to see how you’ve used these tools to conduct analysis and build models. If you have experience with cloud platforms, be sure to include that too!
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 get you on our radar quickly!
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 analysis and predictive modelling. Be ready to discuss specific examples where your insights led to tangible outcomes, especially in credit risk or debt management.
✨Brush Up on SQL and Python Skills
Make sure you’re comfortable with SQL queries and Python for statistical analysis. You might be asked to solve a problem on the spot, so practice writing queries and coding solutions that demonstrate your technical prowess.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about how you would conduct root cause analysis or build predictive models based on hypothetical data sets, and be ready to explain your thought process.
✨Show Your Collaborative Spirit
Since the role involves working cross-functionally, be prepared to discuss how you’ve successfully collaborated with other teams in the past. Highlight any experiences where you translated complex data insights into actionable strategies for non-technical stakeholders.