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- Tasks: Develop and deploy innovative predictive models to transform consumer credit risk assessment.
- Company: Join a progressive fintech credit startup making waves in financial inclusion.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Why this job: Make a real impact on underserved consumers with ethical data science.
- Qualifications: Degree in a quantitative field and 2+ years of relevant experience required.
- Other info: Work in a fast-paced startup with expert leadership and autonomy in projects.
The predicted salary is between 36000 - 60000 £ per year.
This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers. The next step is to speak to Jack.
Company Description: Progressive fintech credit startup
You will develop and deploy innovative predictive models to transform consumer credit risk assessment. By balancing foundational statistics with advanced machine learning and Generative AI, you will tackle challenges in affordability, fraud detection, and entity resolution. Your work directly impacts underserved consumers by creating more equitable financial products through explainable data science.
Location: London, UK
Why this role is remarkable: Rare opportunity to combine traditional linear regression stability with cutting-edge XGBoost and LLM applications in a high-stakes production environment. Join a fast-moving, well-funded startup backed by expert leadership where you have the autonomy to lead projects from experimentation to containerized deployment. Directly contribute to financial inclusion by building ethical models that use SHAP for transparency, ensuring fair outcomes for everyday consumers.
What you will do:
- Build and maintain foundational linear models and advanced XGBoost architectures for credit, affordability, and fraud scoring.
- Implement NLP techniques and LLM context engineering for large-scale entity resolution and unstructured data analysis.
- Collaborate with engineering teams on MLOps to containerize and monitor models within a high-scale AWS cloud environment.
The ideal candidate:
- Holds a degree in a quantitative field with 2+ years of professional experience in Fintech, Finance, or Credit Risk.
- Demonstrates expert-level Python skills and a deep understanding of statistical assumptions, model interpretability, and bias mitigation.
- Possesses a creative, analytical mindset capable of identifying behavioral clusters and trends within raw, complex datasets without predefined hypotheses.
Data Scientist and AI Engineer at progressive fintech credit startup employer: Jack & Jill/External Ats
Contact Detail:
Jack & Jill/External Ats Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist and AI Engineer at progressive fintech credit startup
✨Tip Number 1
Network like a pro! Reach out to people in the fintech space, especially those who work at startups. Use LinkedIn to connect and engage with them; you never know who might have a lead on your dream job.
✨Tip Number 2
Prepare for those interviews! Brush up on your Python skills and be ready to discuss your experience with predictive models and machine learning. Practice explaining complex concepts in simple terms, as communication is key in this role.
✨Tip Number 3
Showcase your projects! If you've worked on any relevant data science or AI projects, make sure to highlight them. Create a portfolio or GitHub repository to demonstrate your skills and thought process.
✨Tip Number 4
Apply through our website! We want to see your application directly, so don’t hesitate to submit your CV and cover letter there. It’s a great way to stand out and show your enthusiasm for the role.
We think you need these skills to ace Data Scientist and AI Engineer at progressive fintech credit startup
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with predictive models and machine learning in your application. We want to see how you've tackled challenges in credit risk assessment or fraud detection before!
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to explain your past projects and how they relate to the role. We appreciate a well-structured application that gets straight to the point.
Tailor Your Application: Don’t just send a generic application! Tailor your CV and cover letter to reflect the specific skills and experiences that match our job description. Show us why you’re the perfect fit for this Data Scientist and AI Engineer role.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at Jack & Jill/External Ats
✨Know Your Models Inside Out
Make sure you can discuss both foundational linear models and advanced techniques like XGBoost. Be ready to explain how you've used these in past projects, especially in relation to credit risk assessment and fraud detection.
✨Showcase Your Python Skills
Prepare to demonstrate your expert-level Python skills. Bring examples of code or projects where you've implemented machine learning algorithms, and be ready to discuss the statistical assumptions behind your models.
✨Understand the Business Impact
Research the fintech landscape and understand how your work can contribute to financial inclusion. Be prepared to discuss how your predictive models can create equitable financial products for underserved consumers.
✨Collaborate and Communicate
Highlight your experience working with engineering teams, especially in MLOps. Be ready to talk about how you've containerised models and monitored them in a cloud environment, as collaboration is key in a fast-moving startup.
