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- Tasks: Join us as a Data Science Consultant, tackling financial challenges with AI-driven solutions.
- Company: We're a top 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 working with cutting-edge AI technologies and a dynamic team.
- Qualifications: 5+ years in data science within financial services; strong communication and technical skills required.
- 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
Network with professionals in the financial services sector. Attend industry conferences, webinars, and meetups to connect with potential colleagues and clients. This can help you gain insights into current challenges and trends, making you a more attractive candidate.
✨Tip Number 2
Stay updated on the latest AI and data science technologies relevant to financial services. Familiarise yourself with tools like Azure, AWS, and GCP, as well as financial regulations such as Basel III. This knowledge will demonstrate your commitment and expertise during interviews.
✨Tip Number 3
Prepare to discuss specific case studies or projects you've worked on that relate to financial services. Highlight your experience with risk modelling, fraud detection, or compliance analytics, as these are key areas of focus for the role.
✨Tip Number 4
Practice articulating complex AI concepts in simple terms. Since you'll be communicating with senior executives, being able to explain technical details clearly will set you apart from other candidates and show your ability to bridge the gap between tech and business.
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, or regulatory compliance.
Craft a Compelling Cover Letter: In your cover letter, explain why you are passionate about data science in the financial sector. Mention specific challenges you've tackled and how your skills align with the company's needs.
Showcase Technical Skills: Clearly list your proficiency in Python and any AI/ML tools you've used, such as Azure or AWS. Provide examples of how you've applied these skills in real-world financial scenarios.
Highlight Communication Abilities: Since the role involves engaging with senior executives, emphasise your communication skills. Share instances where you've successfully presented complex data insights to non-technical stakeholders.
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 risk modeling or fraud detection, and how they relate to the role. This will demonstrate your understanding of the industry's unique challenges.
✨Communicate Complex Ideas Simply
Since you'll be presenting to senior executives, practice explaining complex AI concepts in straightforward terms. Use relatable examples from your past experiences to illustrate your points, ensuring that non-technical stakeholders can grasp the significance of your work.
✨Prepare for Regulatory Discussions
Familiarise yourself with key financial regulations like Basel III and GDPR. Be ready to discuss how you have navigated these regulations in previous roles and how you would implement AI governance frameworks to ensure compliance in your future projects.
✨Demonstrate MLOps Knowledge
Be prepared to talk about your experience with MLOps practices, especially in financial environments. Discuss how you've taken AI/ML models from development to production, and how you've managed model performance issues like explainability and data drift.
