At a Glance
- Tasks: Build predictive models and analyse consumer finance data using AI and statistics.
- Company: Join a fast-moving credit referencing start-up with a progressive culture.
- Benefits: Flexible remote work, collaborative environment, and opportunities for personal growth.
- Other info: Work in a dynamic team with a commitment to diversity and inclusion.
- Why this job: Make a real difference by helping underserved consumers access financial products.
- Qualifications: Degree in a quantitative field and 2+ years in Fintech or Credit Risk.
The predicted salary is between 50000 - 70000 € per year.
Infact are a progressive, fast-moving, credit referencing start-up. We see great opportunities to combine foundational statistics with modern AI to find meaning in consumer finance data. We are looking for a hands-on Data Scientist to work alongside our lead data scientist to experiment, engineer, and deliver innovative predictive models into our modern AWS production environments.
The Technical Reality: We operate a pragmatic stack where Linear Regression remains vital for stability and baseline performance, while XGBoost and LLMs are used as responsible additions. We are looking for someone who knows when to use a simple linear model and when to deploy and how to explain complex non-linear and generative AI.
Current Areas of Focus: Affordability, income and expenditure analysis, credit risk, and fraud detection, with excellence in Entity Resolution – tying together disparate consumer data into a holistic view. Your work will directly help traditionally underserved consumers to access the most suitable financial products, whilst supporting our customers in discovering good responsible actors and highlighting potential risks from others.
Responsibilities
- Predictive Modelling (Linear & Non-Linear): You will build and maintain foundational Linear Regression models for credit, affordability, and fraud scoring, while developing advanced XGBoost models for deeper risk insights. You will mine data to find behavioural signals—such as spending volatility or income stability—that predict affordability, repayment, and fraud risk.
- NLP & Entity Resolution: Use classic NLP techniques (fuzzy matching, named entity recognition) to normalise, cleanse, and match consumer identity data at scale.
- Generative AI & Explainability: Utilise LLM APIs for advanced context engineering on unstructured data, while using models such as SHAP to ensure that every model we build is fair, free from bias, and explainable to consumers, customers, and regulators.
- Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production.
Skills & Requirements
- Core Data Science: Foundational Stats: You must have an excellent grasp of Linear and Logistic Regression. You understand the assumptions, limitations, and interpretability of these models.
- Advanced ML: Experience with boosting models is essential for our higher-complexity tasks.
- Analytics Patterns: A core ability to creatively analyse a raw dataset and spot trends, outliers, and behavioural clusters without needing a pre-defined hypothesis.
- Explainability: Experience using SHAP or similar frameworks to explain model outputs.
- Natural Language Processing (NLP): Entity Matching: Experience with deduplication, record linkage, or entity resolution.
- GenAI: Experience with LLM APIs and Context Engineering (constructing prompts, managing context windows, evaluating behaviour).
- Engineering & Stack: Python: Expert level (Pandas, NumPy, Scikit-Learn). Data Engineering: Strong SQL skills and experience building data pipelines.
Experience:
- Education: Degree in a quantitative field (Statistics, Mathematics, Computer Science, etc.).
- Industry: 2+ years of experience in Fintech, Finance, or Credit Risk is required.
Profile: You are an ambitious candidate who wants to grow. You are comfortable working remotely but value team collaboration.
The Setup
- Location: Primarily remote and flexible, collaborating in the central London office at least 2 days per week.
- Culture: As a small, progressive team, we offer the agility to move fast and the autonomy to lead your own projects.
- Diversity: We are committed to creating a diverse environment and we are proud to be an equal opportunity employer considering candidates without regard to gender, sexual orientation, race, colour, nationality, religion or belief, disability, or age.
See https://infact.io/ for more details about us.
Data Scientist – Credit Risk & AI Innovation in England employer: LinkedIn
Infact is an innovative and dynamic credit referencing start-up that prioritises employee growth and collaboration. With a flexible remote work setup and a vibrant office culture in central London, we empower our Data Scientists to lead projects and experiment with cutting-edge AI technologies while making a meaningful impact on consumer finance. Our commitment to diversity and inclusion ensures a supportive environment where every team member can thrive.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist – Credit Risk & AI Innovation in England
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving predictive modelling and NLP. It’s a great way to demonstrate your expertise beyond the application.
✨Tip Number 3
Prepare for interviews by brushing up on your explainability skills. Be ready to discuss how you’d use SHAP or similar frameworks to make your models understandable to non-techies.
✨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.
We think you need these skills to ace Data Scientist – Credit Risk & AI Innovation in England
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Data Scientist role. Highlight your expertise in Linear Regression, XGBoost, and any relevant projects you've worked on in credit risk or AI innovation.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data science and how you can contribute to our mission. Share specific examples of your work with predictive models and NLP techniques to show us what you bring to the table.
Showcase Your Problem-Solving Skills:In your application, include examples of how you've tackled complex data challenges. We love candidates who can creatively analyse datasets and derive meaningful insights, so don't hold back on sharing your thought process!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s the quickest way for us to see your application and get you into the conversation about joining our innovative team!
How to prepare for a job interview at LinkedIn
✨Know Your Models Inside Out
Make sure you can confidently discuss both Linear Regression and advanced models like XGBoost. Be prepared to explain when to use each model and how they apply to credit risk and fraud detection. This shows you understand the balance between simplicity and complexity in predictive modelling.
✨Showcase Your NLP Skills
Since the role involves entity resolution and NLP, brush up on techniques like fuzzy matching and named entity recognition. Bring examples of how you've used these methods in past projects, especially in cleaning and normalising data. This will demonstrate your hands-on experience and problem-solving skills.
✨Explainability is Key
Familiarise yourself with SHAP or similar frameworks for model explainability. Be ready to discuss how you ensure your models are fair and free from bias. This is crucial for building trust with consumers and regulators, so showing you prioritise this aspect will set you apart.
✨Prepare for Technical Questions
Expect technical questions about Python, SQL, and data engineering. Brush up on your skills with Pandas, NumPy, and Scikit-Learn, and be ready to discuss your experience in building data pipelines. Demonstrating your technical prowess will reassure them that you can hit the ground running.
