AI Consultant

Equifax, Inc.
London
10 months ago
Applications closed

Related Jobs

View all jobs

AI Transformation Consultant: GenAI & MLOps at Scale

Senior Consultant - AI & Data, Financial Services, Data Platforms, Data Engineer, BCM, Edinburgh

Senior Consultant - AI & Data, Financial Services, Data Platforms, Data Engineer, BCM, Edinburgh

Senior Data Engineer & AI Transformation Consultant

Hybrid Data Scientist Consultant — AI & Analytics

Senior AI & Data Engineer - FS Data Platforms

OurAI Consultantroles are unique. The ideal candidate is a rare hybrid, a scientist with strong technical skills in AI and machine learning, 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. They will combine the 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:

  1. Conduct in-depth analysis of data available to Equifax and its partners.

  2. Collaborate with product managers to conduct market research and validate product needs.

  3. Develop and test AI models and algorithms, utilizing platforms like Vertex AI and BQML.

  4. Contribute to the creation of business cases for proposed AI solutions.

  5. Evaluate the feasibility and potential impact of AI projects.

  6. Provide technical guidance and support to junior analysts.

  7. Be proficient in Python, stay up-to-date on the latest advancements in AI and machine learning.

  8. Utilize combined knowledge of data structures, analytics, algorithms/models, and strong computer science fundamentals to independently prepare datasets, conduct analytics, and develop deployable solutions.

  9. Collect, analyze and interpret large data assets to define and build multiple innovative solution components leveraging business and technical expertise. Support the analytical strategy by understanding critical technical capabilities and suggesting opportunities.

  10. Lead the development of projects with multiple deliverables, leveraging business and technical expertise.

  11. Work on high-complexity tasks in problems often within multiple business or analytical domains, collaborating with other teams to develop predictive models, risk assessments, fraud detection, recommendation engines, etc., encouraging enhanced solutions.

  12. Package, summarize, visualize, and perform storytelling on analytical findings and results for management and business users.

  13. Communicate results to external stakeholders and mid-level leadership, able to communicate the business impact of work.

  14. Evaluate the technical work of peers and junior data scientists, guiding them on deliverable quality and accuracy.


What experience you need:

  1. Bachelor's degree (2:1 or above) in a numerical subject (Computer Science, Mathematics, Statistics, Physics, Engineering).

  2. Solid experience in data analysis, machine learning, and AI development.

  3. Hands-on experience with cloud-based AI platforms and tools.

  4. Proficiency in programming languages such as Python and SQL.

  5. Strong analytical and problem-solving skills.

  6. Ability to work independently and as part of a team.

  7. Good communication, presentation, and visualization skills.

  8. Strong experience in a related analytical role.

  9. Proven track record of designing and developing predictive models in real-world applications.

  10. Experience with model performance evaluation and predictive model optimization for accuracy and efficiency.

  11. Cloud certification strongly preferred.

  12. Additional role-based certifications may be required depending upon region/BU requirements.


What could set you apart:

  1. Experience with specific AI techniques, such as neural networks or natural language processing.

  2. Knowledge of the financial services industry.

  3. Contributions to open-source AI projects.

  4. Experience with data visualization tools.

  5. Passion for data science, data mining, machine learning, and experience with big data architectures and methods.

  6. A Master's degree in a quantitative field (Statistics, Mathematics, Economics).

#J-18808-Ljbffr

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.