Job Description

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 and 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.

Job Type: Full-time

Pay: 60,000.00-90,000.00 per year

Benefits:

Casual dress Company pension Cycle to work scheme Employee stock ownership plan Health & wellbeing programme Private dental insurance Private medical insurance Work from home
Experience:

credit risk: 1 year (required)
Work authorisation:

United Kingdom (required)
Work Location: Hybrid remote in London EC1R 0DE