Principal Data Science Consultant - Financial Services Expertise

EPAM
Manchester
9 months ago
Applications closed

Related Jobs

View all jobs

Principal, AI Data Science

Data Analyst - Aerospace

Data Analyst - Sc cleared

As one of the world's leading digital transformation service providers, we are looking to enhance our Data Practice across Europe to meet the increasing client demand for our Data Science and AI services. We are seeking a highly skilled and experienced

Data Science Consultant

to join our team.

The ideal candidate will have a strong background in data science, analytics, IT consulting, and domain expertise in financial services. As a Data Science Consultant, you will work closely with clients to understand their business challenges, design and implement data-driven solutions, and provide actionable insights that drive business value. Your ability to address challenges specific to financial services, such as risk modeling, fraud detection, and regulatory compliance, will be a critical asset.

#LI-DNI

Responsibilities

Support financial services clients with the definition and implementation of their AI strategy, focusing on areas such as risk management, customer analytics and operational efficiency
Implement and oversee AI governance frameworks, with an emphasis on regulatory compliance (e.g., Basel III, GDPR) and ethical AI principles
Ideate, design and implement AI-enabled solutions for financial services use cases, such as credit scoring, fraud detection, customer segmentation and predictive modeling
Lead the process of taking AI/ML models from development to production, ensuring robust MLOps practices tailored to financial data environments
Monitor and manage model performance, including addressing issues related to explainability, data drift and model drift in financial models
Collaborate with risk, compliance and legal teams to navigate financial regulations and ensure models meet stringent industry standards
Engage with senior executives, effectively communicating AI opportunities, risks and strategies in accessible terms, particularly in the financial services context
Maintain up-to-date knowledge of industry trends, emerging technologies and regulatory changes impacting AI/ML in financial services
Support pre-sales activities, including client presentations, demos and RFP/RFI responses tailored to financial services prospects
Requirements
Bachelors or Masters degree in Data Science, Computer Science, Statistics, Mathematics, Finance, Economics or a related field
5+ years of experience in data science, analytics or related roles within the financial services industry or IT consulting for financial institutions
Strong communication skills, comfortable presenting to senior business leaders in banking, insurance or investment firms
Proven experience in financial services data science projects, such as credit risk modeling, anti-money laundering (AML) systems or algorithmic trading models
Familiarity with key financial industry regulations, such as Basel III, Solvency II, MiFID II or the EU AI regulatory framework
Deep understanding of LLMs and their application in areas like financial document analysis, customer service chatbots or regulatory reporting
Expertise in fraud detection techniques, anomaly detection and compliance analytics
Strong understanding of ML Ops principles and experience in deploying and managing AI/ML models in financial systems
Proficiency in Python and familiarity with AI/ML tools and platforms such as Azure, AWS, GCP, Databricks, MLFlow, Airflow and financial-specific platforms like Bloomberg Terminal, SAS, or MATLAB
Experience with structured and unstructured financial data, including time-series analysis, market data and transactional data
Ability to articulate complex AI risks and strategies to non-technical stakeholders, including senior executives in banking and insurance
Nice to have
Ph.D. in Data Science, Computer Science, Statistics, Mathematics, Finance, Economics or a related field
Expertise in stress testing models, scenario analysis and portfolio optimization
We offer
EPAM Employee Stock Purchase Plan (ESPP)
Protection benefits including life assurance, income protection and critical illness cover
Private medical insurance and dental care
Employee Assistance Program
Competitive group pension plan
Cyclescheme, Techscheme and season ticket loans
Various perks such as free Wednesday lunch in-office, on-site massages and regular social events
Learning and development opportunities including in-house training and coaching, professional certifications, over 22,000 courses on LinkedIn Learning Solutions and much more
If otherwise eligible, participation in the discretionary annual bonus program
If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program
*All benefits and perks are subject to certain eligibility requirements

#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.

Jobs

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.