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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 evaluating statistical and machine learning models, with a strong focus on data quality, exploratory analysis, and effective communication of complex concepts. Proficient in Python, SQL, and dbt, with a commitment to maintaining high standards in documentation and model governance.
Highest-signal resume keywords
Statistical And Machine Learning Model DevelopmentPython ProgrammingSQL And dbt ProficiencyData Cleaning And Quality AssuranceModel Governance And Regulatory Compliance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Statistical ModelingMachine LearningData ExplorationFeature EngineeringModel EvaluationPerformance MonitoringProbability And Statistical InferenceData TransformationData AnalysisData Visualization
Soft Skills
Analytical Problem SolvingEffective CommunicationAttention To DetailProject ManagementCollaboration
Industry Keywords
Credit RiskFraud DetectionCustomer EngagementOperational EfficiencyRisk ManagementComplianceAuditFinancial Services
Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Build and iterate statistical and machine learning models for credit risk, fraud, customer engagement, and operational efficiency
- Partner with stakeholders to define problem statements, success metrics, data requirements, and implementation plans
- Conduct data exploration and feature engineering to identify outcome drivers and improve model performance and interpretability
- Develop evaluation frameworks with baselines, validation strategies, monitoring metrics, and performance reporting
- Support production model deployment with Engineering through reproducible pipelines and model documentation
- Monitor production models, identify performance drift, and support recalibration or retraining
- Apply probability and statistical inference to design experiments, interpret results, and provide recommendations
- Identify data quality issues and support data cleaning, normalization, and reliable dataset standards
- Write maintainable, well-tested Python code using common data science libraries
- Use SQL and dbt to extract, transform, and validate data for analysis and modelling
- Collaborate with Risk, Compliance, and Audit stakeholders on governance, documentation, and regulatory alignment
- Support continuous improvement of data science methodologies, tooling, and ways of working
Requirements
What you’ll need- Experience building and evaluating statistical and machine learning models in a commercial environment
- Strong analytical and problem solving skills
- Ability to translate business challenges into practical data science solutions
- Ability to independently source and define required datasets, clarify data needs, and ensure appropriate coverage and traceability
- Ability to independently apply robust data cleaning approaches and identify data quality issues
- Ability to perform exploratory analysis and statistical investigation and translate findings into actionable recommendations
- Experience writing maintainable Python code for analysis and modelling
- Good practices in testing and documentation
- Experience using SQL and dbt to extract, transform, validate, and analyse data
- Ability to produce clear, purposeful visualisations and model performance summaries
- Ability to explain complex analytical concepts clearly to technical and non-technical stakeholders
- Strong attention to detail, with validated, reproducible, and documented outputs
- Ability to plan and deliver data projects to agreed timelines and manage competing priorities
- Experience working collaboratively with Product, Risk, Operations, Compliance, and Engineering teams is beneficial
- Awareness of model governance, risk management, and regulatory considerations in financial services is advantageous
Benefits
Comp & perks- Hybrid/flexible working policy
- Freedom for individuals and teams to use the London office space to boost productivity
- Collaborative London office resource
- Reasonable adjustments available to all candidates throughout the recruitment process