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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and leading ML and MLOps functions, with a focus on developing credit and decisioning models, implementing responsible AI practices, and establishing rigorous validation methodologies. Proven ability to manage teams, budgets, and model-risk governance in high-stakes environments.
Highest-signal resume keywords
ML Lifecycle ManagementCredit-Scoring ModellingModel-Risk ManagementTeam LeadershipResponsible AI Governance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
ML DevelopmentModel ValidationModel DeploymentModel MonitoringModel RetrainingChampion/Challenger TestingExplainability ChecksFeature Store ManagementFraud-Detection MLCompute Budget Management
Soft Skills
LeadershipCollaborationCommunication
Tools & Technologies
DatabricksSpark
Certifications & Qualifications
CCD2
Industry Keywords
Consumer-Credit RegulationDORA/ICT RiskIFRS 9
Tech Stack
Tools & technologiesSpark
About the role
Key responsibilities & impact- Build the ML and MLOps function from zero
- Build a state-of-the-art ML platform and the discipline around it
- Develop credit and decisioning models with rigorous validation, champion/challenger testing and explainability
- Engineer production ML serving, monitoring, reproducibility and retraining
- Implement responsible AI, including model-risk, bias and explainability checks with an independent sign-off gate before production
- Build agent-first production systems with orchestration, guardrails, evaluations and observability
- Use a point-in-time-correct feature store and governed data
- Own the ML & MLOps team from the first hire onward
- Establish model-development standards and validation methodology for model-risk and regulatory scrutiny
- Own the ML platform behind decisioning services
- Define ownership between feature production and model consumption with Data Engineering leadership
- Manage compute budget, headcount and return on investment
Requirements
What you’ll need- Experienced across the full ML lifecycle: development, validation, deployment, monitoring and retraining
- Experienced in credit-scoring or underwriting modelling, or comparable high-stakes ML
- Skilled in model-risk management and responsible-AI governance
- Experienced in building and leading a team from zero
- Fluent in English (B2+)
- Suggested 7+ years in ML and 3+ years leading
- Bonus: CCD2 and consumer-credit regulation
- Bonus: DORA/ICT risk
- Bonus: IFRS 9 implications for model outputs
- Bonus: fraud-detection ML
- Bonus: Databricks/Spark
Benefits
Comp & perks- Seat at the table on a core leadership team
- Build it right the first time with no legacy ML estate
- Real pace in a lean, AI-native organisation
- Employees can work remotely