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 diverse team that values innovation and autonomy.
- Why this job: Make a real impact by helping underserved consumers access suitable financial products.
- Qualifications: Degree in a quantitative field and 2+ years in Fintech or Credit Risk.
The predicted salary is between 36000 - 60000 £ 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 London employer: Infact
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 London
✨Tip Number 1
Network like a pro! Reach out to people 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 projects, especially those related to predictive modelling and NLP. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to explain your thought process behind model selection and how you ensure explainability in your work. Practice common data science interview questions to boost your confidence.
✨Tip Number 4
Apply through our website! It’s the best way to get noticed. Tailor your application to highlight your experience with linear regression and advanced ML techniques, and don’t forget to mention your passion for helping underserved consumers.
We think you need these skills to ace Data Scientist – Credit Risk & AI Innovation in London
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. Mention specific examples of how you've used predictive modelling or NLP techniques in your previous roles.
Showcase Your Projects:If you've worked on any interesting projects, especially those involving generative AI or entity resolution, make sure to include them. We love seeing practical applications of your skills, so don't hold back!
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’re considered for the role. Plus, it shows us you’re keen to join our team!
How to prepare for a job interview at Infact
✨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.
✨Prepare for Explainability Questions
Expect questions about model explainability, particularly using frameworks like SHAP. Be ready to discuss how you ensure your models are fair and free from bias, and how you communicate these concepts to non-technical stakeholders. This is crucial in a role that impacts consumer finance.
✨Emphasise Team Collaboration
As the position values teamwork, share experiences where you've successfully collaborated with others, especially in remote settings. Highlight your ability to lead projects while also being a supportive team member. This will resonate well with their culture of agility and autonomy.
