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- Tasks: Join us as a Data Science Consultant to tackle financial services challenges with AI-driven solutions.
- Company: Be part of a leading digital transformation service provider enhancing data practices across Europe.
- Benefits: Enjoy perks like private medical insurance, free lunches, and extensive learning opportunities.
- Why this job: Make an impact in financial services while collaborating with top executives and navigating industry regulations.
- Qualifications: Bring 5+ years of experience in data science and a strong background in financial services.
- Other info: Opportunities for professional growth and participation in employee stock purchase plans.
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.
#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
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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. Highlight specific examples of risk modeling, fraud detection, or compliance analytics that you've worked on, as these are critical areas for this role.
✨Tip Number 2
Familiarize yourself with key financial regulations like Basel III and GDPR. Being able to discuss how these regulations impact AI strategies will demonstrate your understanding of the industry and your ability to navigate complex compliance landscapes.
✨Tip Number 3
Prepare to discuss your approach to MLOps and how you ensure robust deployment and management of AI/ML models in financial environments. This is essential for demonstrating your technical expertise and operational readiness.
✨Tip Number 4
Engage with current trends in AI and financial services. Being knowledgeable about emerging technologies and their applications can set you apart and show your commitment to staying ahead in the field.
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 specific projects you've worked on, such as risk modeling or fraud detection, and quantify your achievements where possible.
Craft a Compelling Cover Letter: In your cover letter, emphasize your understanding of the financial services industry and how your skills align with the company's needs. Discuss your experience with AI governance frameworks and regulatory compliance, as these are crucial for the role.
Showcase Relevant Skills: Clearly list your technical skills, especially in Python and any AI/ML tools you are proficient in. Mention your familiarity with financial regulations and how you've applied this knowledge in past roles.
Prepare for Interviews: Be ready to discuss your previous projects in detail, particularly those related to financial services. Prepare to explain complex AI concepts in simple terms, as you'll need to communicate effectively with senior executives.
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 Clearly with Non-Technical Stakeholders
Since you'll be engaging with senior executives, practice explaining complex AI concepts in simple terms. Use relatable examples from your past experiences to demonstrate your ability to bridge the gap between technical and non-technical audiences.
✨Demonstrate Your Knowledge of Regulatory Compliance
Familiarize yourself with key regulations like Basel III and GDPR. Be prepared to discuss how you have implemented AI governance frameworks in previous roles and how you ensure compliance in your data science projects.
✨Prepare for Technical Questions on MLOps
Expect questions about your experience with MLOps practices, especially in financial environments. Be ready to discuss how you've taken AI/ML models from development to production and how you monitor their performance over time.
