Principal Data Science Consultant - Financial Services Expertise

EPAM
London
1 day ago
Create job alert

As one of the worlds 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 experiencedData Science Consultantto 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.

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

Related Jobs

View all jobs

Principal Data Consultant (all genders)

Principal Data Scientist, Consulting

Wastewater Network Modeller - all grades

Wastewater Network Modeller - all grades

Wastewater Network Modeller - all grades

Wastewater Network Modeller - all grades

Get the latest insights and jobs direct. Sign up for our newsletter.

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 Leadership for Managers: Strategies to Motivate, Mentor, and Set Realistic Goals in Data-Driven Teams

Machine learning (ML) has become an indispensable force in the modern business world, influencing everything from targeted marketing campaigns to advanced medical diagnostics. As industries integrate predictive algorithms and data-driven decision-making into their core operations, the need for effective leadership in machine learning environments has never been greater. Whether you’re overseeing a small team of data scientists or spearheading an enterprise-scale ML project, your leadership style must accommodate rapid innovation, complex problem-solving, and diverse stakeholder expectations. This guide provides actionable insights into how you can motivate, mentor, and establish achievable goals for your machine learning teams—ensuring they thrive in data-driven environments.

Top 10 Books to Advance Your Machine Learning Career in the UK

Machine learning (ML) remains one of the fastest-growing fields within technology, reshaping industries across the UK from finance and healthcare to e-commerce, telecommunications, and beyond. With increasing demand for ML specialists, job seekers who continually update their knowledge and skills hold a significant advantage. In this article, we've curated ten essential books every machine learning professional or aspiring ML engineer in the UK should read. Covering foundational theory, practical implementations, advanced techniques, and industry trends, these resources will equip you to excel in your machine learning career.

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.