Your new company
Reputable financial services organisation.
What you'll need to succeed
Technical Experience:
1. Machine Learning & Analytics
2. Strong experience in developing ML solutions for financial or risk management domains (e.g., credit risk, market risk, operational risk, liquidity, financial forecasting, fraud detection).
3. Expertise in statistical modelling, predictive analytics, and supervised/unsupervised ML methods (e.g., regressions, classification, time-series forecasting, clustering, anomaly detection, NLP techniques).
4. Data Engineering & Infrastructure
5. Demonstrable expertise in building data pipelines for large-scale historical financial datasets.
6. Familiarity with big-data and distributed technologies such as Spark, Hadoop, Databricks, or Snowflake.
7. Strong SQL and data querying capabilities.
8. Competent programming skills (Python mandatory; R or Java/Scala helpful).
9. ML Engineering & Deployment:
10. Experienced in modern MLOps tools and methods (e.g., MLFlow, Kubeflow).
11. Skilled in containerization and orchestration technologies (Docker, Kubernetes).
12. Familiar with automated CI/CD pipelines for ML deployment and model lifecycle management.
Business & Industry Knowledge:
13. Deep understanding of banking/financial services industry (especially Risk, Finance, Regulatory compliance).
14. Familiarity with regulatory requirements (e.g., Basel III/IV, IFRS9, Stress Testing, Model Risk Management principles).
15. Experience dealing with cross-functional stakeholders including Risk Management, Finance, Regulatory Reporting, and Internal Audit.
16. Soft Skills & Leadership:
17. Excellent stakeholder management skills, able to influence and collaborate across Risk, Finance, Data teams, IT, and business units.
18. Proven leadership ability to build, motivate, and manage highly skilled technical teams.
19. Strong communication skills, translating complex concepts into clear explanations for non-technical audiences.
20. Comfortable in ambiguous and evolving scenarios, able to rapidly pivot to business priorities.
Education & Professional Background:
21. Master's or PhD in a quantitative discipline (Statistics, Mathematics, Physics, Economics, Computer Science, Engineering).
22. 7–10+ years of relevant industry experience with at least 3–5 years in a leading or senior role in ML projects.
What you'll get in return
23. Pay rate between €500-€700 per day (subject to experience).
24. 6-Month Contract.
25. Opportunity to work with a reputable Fortune 100 organisation within the financial industry.