Senior Data Science Consultant

Experian Information Solutions, Inc.
Nottingham
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Science Consultant

Senior Data Engineering Consultant

Senior Consultant, Data Science (Customer Data)

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

We have a new vacancy for an experienced Senior Data Science / Analytics Consultant to join our Analytics team and support with our cloud-based Ascend platform. You will partner with clients to understand their business, identify what data is required and how clients can best use Experian data analytics to improve business outcomes.


Responsibilities

  • Design analytics solutions to client's problems in any area of consumer lending and credit risk management, using Experian analytics solutions.
  • Engage in a consultative way with the client, to identify problems and define, design and deliver analytics solutions, with expertise in credit risk modelling and optimisation techniques.
  • Present proposals to clients for analytics solutions, including recommendations.
  • Provide consultancy on the potential bigger picture strategies.
  • Coordinate with Experian’s Analytics Pre‑Sales team to contribute to sales opportunities and support the conversion of sales prospects.

Qualifications

  • Strong analytical modelling and consultancy experience gained in credit risk management or banking sector as a Consultant, Data Scientist or Machine Learning Engineer.
  • Applied modelling and analytics experience to lead business decisions.
  • Expertise in credit risk decisioning.
  • Deep coding knowledge in Python with SAS or R.
  • Good stakeholder management skills.
  • Subject matter expert on the mechanics of consumer lending (risk, data usage, outcomes).
  • Knowledge of Cloud/AWS.
  • Product strategy experience desirable but not essential.

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realise their financial goals and help them save time and money. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments. We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com.


Benefits

  • Hybrid working
  • Great compensation package
  • Core benefits include pension, Bupa healthcare, ShareSave scheme and more.
  • 25 days annual leave with 8 bank holidays and 3 volunteering days. You can purchase additional annual leave.

Our uniqueness is that we celebrate you. Experian’s culture and people are important differentiators. We take our people agenda very seriously and focus on what truly matters: DEI, work/life balance, development, authenticity, engagement, collaboration, wellness, reward & recognition, volunteering, and more. Experian’s people‑first approach is award‑winning; Great Place To Work in 24 countries, FORTUNE Best Companies to work and Glassdoor Best Places to Work (globally 4.4 Stars). Experian is proud to be an Equal Opportunity and affirmative action employer. Innovation is a critical part of Experian’s DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.


Grade: C/EB7 #LI-DSI #LI-Hybrid Experian Careers - Creating a better tomorrow together.


#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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.