Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist

Equifax
London
3 days ago
Create job alert
Overview

Come join the Equifax UK Product Analytics & Innovation team and develop market-leading scores, models and analytical solutions using the latest cloud-based technologies, techniques and tooling. Be part of a growing and diverse team tasked with creating the next generation of statistical models, machine-learning algorithms and AI-based products and services. As an Equifax Data Scientist, you will play a pivotal role in the Product Analytics & Innovation team. You will work closely with internal clients and stakeholders to proactively understand their challenges, propose and develop solutions and lead the execution of analytical and consultancy projects, including the design and development of complex modelling assignments utilising CRA data. You will have frequent engagements with stakeholders and will manage multiple analytics and consultancy projects. Our Data Scientist roles are unique. The ideal candidate is a rare hybrid; a scientist with the programming abilities to scrape, combine, and manage data from a variety of sources and a statistician who knows how to derive insights from the information within. He or she will combine these skills to create new prototypes with the creativity and thoroughness to ask and answer the deepest questions about the data, what secrets it holds, and to push the boundaries of what is possible with big data. Want to know more?

What You\'ll Do:

Responsibilities
  • Strategic Data Utilisation: Enable the organisation to make better decisions, innovate, and grow by effectively using its data assets.
  • End-to-End Solution Development: Develop and deploy solutions by independently preparing datasets, performing analysis, and building predictive models, risk assessments, and other analytical tools.
  • Data Preparation and Engineering: Create data pipelines to collect, integrate, consolidate, cleanse, and structure large, complex datasets from various sources for analytical use.
  • Analytical Strategy & Innovation: Support the overall analytical strategy by understanding technical capabilities and suggesting opportunities for new, enhanced solutions.
  • Data Analysis & Interpretation: Analyse and interpret large data assets to create multiple innovative solution components, applying both business and technical expertise.
  • Problem-Solving & Collaboration: Work on highly complex problems across multiple domains, collaborating with other teams to develop advanced solutions such as fraud detection and recommendation engines.
  • Communication & Storytelling: Summarise, visualise, and present analytical findings and results to management and business users in a clear, compelling way.
  • Data Quality & Governance: Develop rules and tracking processes to maintain high data quality, and implement improvements based on best practices for data management and security.
  • Staying Current & Proposing Solutions: Keep up with the latest trends and advancements in cloud platforms (like GCP) and related technologies, actively proposing and evaluating new solutions.
  • Mentorship & Quality Assurance: Guide and mentor junior Data Scientists, and review their work to ensure the quality of their dataset implementations.
  • Communicate results to external stakeholders and mid level leadership, able to communicate business impact of work
Qualifications
  • BSc degree in a STEM major or equivalent discipline
  • Extensive & current experience in a related analytical role
  • Held a similar analytical position in a commercial lending business or a similar business to Equifax
  • Extensive exposure to commercial data assets e.g Companies House data at a minimum
  • Experience building Commercial Credit Scores including risk, PD and business failure scores
  • Advanced skills using programming languages such as Python or SQL, and intermediate level experience with scripting languages
  • Proven track record of designing and developing predictive models in real-world applications
  • Experience with model performance evaluation and predictive model optimisation for accuracy and efficiency
  • Additional role-based certifications may be required depending upon region/BU requirements
  • Experience building and maintaining moderately-complex data pipelines, troubleshooting issues, transforming and entering data into a data pipeline in order for the content to be digested and usable for future projects
  • Experience designing and implementing complex data models and experience enabling optimisation to improve performance
What could set you apart
  • Cloud experience using GCP or Amazon AWS
  • Exposure to machine learning techniques
  • Exposure to model implementation and testing techniques
  • Google Cloud Certification
  • Experience navigating the security governance arena
  • Deep understanding of the industry / regulatory landscape, particularly for commercial business lending
  • Passion for data science, data mining, machine learning and experience with big data architectures and methods
  • A Master\'s degree in a quantitative field (Statistics, Mathematics, Economics)


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Optimisation)

Data Scientist - Tax & Legal

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.