Data Scientist and AI Engineer at infact.io at Jack & Jill/External ATS

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

05 Jun, 26

Salary

0.0

Posted On

07 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Predictive Models, Machine Learning, Generative Ai, Statistics, XGBoost, LLM Applications, NLP Techniques, Entity Resolution, MLOps, AWS Cloud, Python, Model Interpretability, Bias Mitigation, SHAP, Linear Regression, Fraud Detection

Industry

Staffing and Recruiting

Description

This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers. She will pick the best candidates from Jack's network. The next step is to speak to Jack. Data Scientist and AI Engineer Company Description: Progressive fintech credit startup Job Description: 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. Who are Jack & Jill? Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support. And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate. Next steps Step 1. Visit our website. Step 2. Click 'Talk to Jack'. Step 3. Talk to Jack so he can understand your experience and ambitions. Step 4. Jack will make sure Jill (the AI agent working for the company) considers you for this role. Step 5. If Jill thinks you're a great fit and her client wants to meet you, they will make the introduction. Step 6. If not, Jack will find you excellent alternatives. All for free. We never post fake jobs This isn't a trick. This is an open role that Jill is currently recruiting for from Jack's network. Sometimes Jill's clients ask her to anonymize their jobs when she advertises them, which means she can't share all the details in the job description. We appreciate this can make them look a bit suspect, but there isn't much we can do about it. Give Jack a spin! You could land this role. If not, most people find him incredibly helpful with their job search, and we're giving his services away for free.

Responsibilities

The role involves developing and deploying innovative predictive models, balancing foundational statistics with advanced machine learning and Generative AI to assess consumer credit risk, affordability, and fraud. Responsibilities include building and maintaining linear and XGBoost models, implementing NLP/LLM techniques for entity resolution, and collaborating on MLOps for containerized deployment in AWS.