At a Glance
- Tasks: Create innovative predictive models using AI and ML to analyse consumer finance data.
- Company: Join a fast-moving credit referencing start-up with a progressive culture.
- Benefits: Flexible remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative team environment with a commitment to diversity and inclusion.
- Why this job: Make a real difference by helping underserved consumers access suitable financial products.
- Qualifications: 2+ years in Fintech or Finance, strong Python and SQL skills required.
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.
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.
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.
- 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.
Core Data Science: Advanced ML: Experience with boosting models is essential for our higher-complexity tasks. Natural Language Processing (NLP): GenAI: Experience with LLM APIs and Context Engineering (constructing prompts, managing context windows, evaluating behaviour). Python: Expert level (Pandas, NumPy, Scikit-Learn). Data Engineering: Strong SQL skills and experience building data pipelines. Degree in a quantitative field (Statistics, Mathematics, Computer Science, etc.). Industry: 2+ years of experience in Fintech, Finance, or Credit Risk is required.
You are comfortable working remotely but value team collaboration. Primarily remote and flexible, collaborating in the central London office at least 2 days per week. 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.
Data Scientist (AI/ML) employer: Infact
Infact is an excellent employer for Data Scientists looking to make a meaningful impact in the fintech sector. With a commitment to innovation and diversity, we offer a collaborative work culture that values autonomy and encourages professional growth through hands-on experience with cutting-edge AI technologies. Our flexible remote working arrangements, combined with the opportunity to engage with a dynamic team in our central London office, create a unique environment where you can thrive and contribute to empowering underserved consumers.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist (AI/ML)
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow data enthusiasts. 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 involving predictive modelling and NLP. This will give potential employers a taste of what you can do and how you approach problem-solving.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to explain complex concepts like linear regression and LLMs in simple terms. Practice common interview questions and think about how your experience aligns with the role.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive and engaged with our mission to help underserved consumers.
We think you need these skills to ace Data Scientist (AI/ML)
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 predictive modelling, NLP, and any relevant projects you've worked on. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about combining statistics with AI in consumer finance. Share specific examples of your work that demonstrate your ability to tackle challenges like credit risk and fraud detection.
Showcase Your Technical Skills:Don’t forget to mention your technical prowess! Whether it's Python, SQL, or experience with LLM APIs, make sure we know what tools you’re comfortable with. We love seeing candidates who can hit the ground running with our tech stack.
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. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at Infact
✨Know Your Models Inside Out
Make sure you can explain the differences between Linear Regression, XGBoost, and LLMs clearly. Be ready to discuss when to use each model and how they apply to real-world scenarios in credit risk and fraud detection.
✨Showcase Your Data Skills
Prepare to demonstrate your expertise in Python, SQL, and data engineering. Bring examples of data pipelines you've built or predictive models you've developed, especially those that relate to consumer finance.
✨Understand the Business Impact
Be ready to talk about how your work as a Data Scientist can help underserved consumers access financial products. Think about specific examples where your models have made a difference in affordability or risk assessment.
✨Emphasise Team Collaboration
Since this role involves working closely with a small team, highlight your experience in collaborative projects. Share how you’ve contributed to team success and how you value input from others in a remote setting.
