Data Scientist

Infact
Bristol, United Kingdom
4 days ago

Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
£ 147K

Job location

Remote
Bristol, United Kingdom

Tech stack

API
Artificial Intelligence
Amazon Web Services (AWS)
Data analysis
Data Deduplication
Information Engineering
Data Mining
Fraud Prevention and Detection
Python
Linear Regression
Logistic Regression
Machine Learning
Natural Language Processing
Named Entity Recognition
NumPy
SQL Databases
Unstructured Data
Large Language Models
Generative AI
Pandas
Core Data
Scikit Learn
Information Technology
XGBoost
Machine Learning Operations
Data Pipelines

Job description

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., * 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., * 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. Similar jobs, A progressive fintech credit startup in Greater London is seeking a Data Scientist and AI Engineer to develop innovative predictive models for consumer credit risk assessment. This role involves balancing statistics with advanced machine learning to enhance financial...

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., * 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., Job Description Data Scientist - Credit Risk £55-65,000 London THE COMPANY This business are a dynamic and fast-paced lender and are seeking a driven and experienced individual to join their team in building out their predictive models using cutting-edge Machine Learning...

Benefits & conditions

Job Description Data Scientist (NLP & LLM Specialist)Remote- UK 6-month contract with Potential ExtensionDay rate - £427.68 - £565 per day Inside IR35 Data Scientist (NLP & LLM Specialist) Are you an expert in Natural Language Processing who thrives on building scalable,...

About the company

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