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For Companies 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.
- 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.
- Other info: Work in a dynamic team with a commitment to diversity and inclusion.
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 employer: Infact
Contact Detail:
Infact Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Credit Risk & AI Innovation
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. 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 projects, especially those related to predictive modelling and AI. This will give potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to explain your thought process behind model choices and how you tackle data challenges.
✨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
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the job description. Highlight your expertise in foundational statistics and advanced ML techniques, as well as any relevant projects you've worked on.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about credit risk and AI innovation. Share specific examples of how you've used predictive modelling or NLP in your previous roles to make an impact.
Showcase Your Technical Skills: Don’t forget to mention your proficiency in Python, SQL, and any experience with MLOps. We want to see how you can contribute to our engineering and deployment processes, so be clear about your technical capabilities.
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 this exciting opportunity 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.
✨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.
✨Emphasise Team Collaboration
Even though the role is primarily remote, highlight your experience working collaboratively in teams. Share examples of how you've contributed to team projects, especially in a fast-paced environment. This aligns with their culture of agility and teamwork, making you a more attractive candidate.
