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- Tasks: Support financial services clients with AI strategy and implement data-driven solutions.
- Company: Join a leading digital transformation service provider enhancing Data Practice across Europe.
- Benefits: Enjoy perks like private medical insurance, employee stock purchase plan, and free lunches.
- Why this job: Be part of impactful projects in financial services, driving innovation and compliance.
- Qualifications: 5+ years in data science with expertise in financial services and strong communication skills required.
- Other info: Opportunities for learning and development with access to over 22,000 courses.
The predicted salary is between 43200 - 72000 £ per year.
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.
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
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Principal Data Science Consultant - Financial Services Expertise employer: EPAM
Contact Detail:
EPAM Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Data Science Consultant - Financial Services Expertise
✨Tip Number 1
Make sure to showcase your experience in financial services data science projects during the interview. Be prepared to discuss specific examples of risk modeling, fraud detection, or compliance analytics that you've worked on.
✨Tip Number 2
Familiarize yourself with the latest regulations and compliance standards in the financial industry, such as Basel III and GDPR. This knowledge will help you demonstrate your understanding of the challenges clients face and how you can address them.
✨Tip Number 3
Prepare to articulate complex AI concepts in simple terms. Since you'll be communicating with senior executives, practice explaining AI opportunities and risks in a way that resonates with non-technical stakeholders.
✨Tip Number 4
Stay updated on emerging technologies and trends in AI and financial services. Being knowledgeable about the latest tools and platforms will not only impress your interviewers but also show your commitment to continuous learning.
We think you need these skills to ace Principal Data Science Consultant - Financial Services Expertise
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data science and financial services. Focus on relevant projects, especially those involving risk modeling, fraud detection, and compliance analytics.
Craft a Compelling Cover Letter: In your cover letter, emphasize your understanding of AI governance frameworks and your ability to communicate complex AI strategies to non-technical stakeholders. Mention specific examples from your past work that align with the job requirements.
Showcase Technical Skills: Clearly list your proficiency in Python and any AI/ML tools you have used, such as Azure or AWS. Highlight your experience with financial data and MLOps practices, as these are crucial for the role.
Prepare for Interviews: Be ready to discuss your previous projects in detail, particularly those related to financial services. Prepare to articulate how you would approach challenges like model performance monitoring and regulatory compliance in AI solutions.
How to prepare for a job interview at EPAM
✨Showcase Your Financial Services Expertise
Make sure to highlight your experience in financial services during the interview. Discuss specific projects you've worked on, such as credit risk modeling or fraud detection, and how they relate to the role.
✨Communicate Complex Concepts Simply
Since you'll be presenting to senior executives, practice explaining complex AI and data science concepts in simple terms. Use relatable examples from your past experiences to illustrate your points.
✨Demonstrate Your MLOps Knowledge
Be prepared to discuss your understanding of MLOps principles and how you've implemented them in previous roles. Share examples of how you've taken AI/ML models from development to production, especially in financial contexts.
✨Stay Updated on Industry Trends
Research current trends and regulations in the financial services industry, such as Basel III and GDPR. Being knowledgeable about these topics will show your commitment to the field and help you engage in meaningful discussions.
