Skills and Qualifications

• 4+ years of hands-on data science / applied ML in production environments

• Strong Python (pandas, scikit-learn, numpy) and SQL — you can go from raw data to deployed model without waiting on engineering

• Deep practical experience with classifier and regression modeling — feature engineering, model selection, calibration, evaluation under class imbalance

• Solid applied statistics: hypothesis testing, regression, experimental design, dealing with selection bias and censored outcomes

• Experience working with messy real-world financial data (transactional data, bank statements, payments, credit bureau data) — or strong evidence you can ramp on it quickly

• Comfort with relational databases (Postgres / MySQL) and modern data tools

• Strong written and verbal communication — you can explain a model's behaviour to a credit officer, a marketer, and an engineer in the same week

Credit & underwriting**

• Build, deploy, and continuously improve credit scoring models using bank statement data, payment

• histories, CRB pulls, and in-app behavioural signals

• Design automated underwriting flows that serve both digitally acquired customers and salessourced applications

• Implement model retraining pipelines so scoring improves as we accumulate repayment outcomess - not as a quarterly project

• Own model performance monitoring, drift detection, and automated alerting

• Partner with Risk and Operations on policy thresholds, override rules, and the human-in-the-loop processes that wrap the models

Growth & marketing analytics**

• Optimize ad targeting across our acquisition channels — audience selection, bid strategy, creative performance, lookalike construction

• Instrument and analyze the acquisition funnel end-to-end (impression → click → install → KYC → first loan → repayment)

• Design and run A/B tests on acquisition and product experiences; build the experimentation infrastructure so the team can run tests without you

• Build attribution and LTV/CAC models that the business can actually act on Cross-cutting

• Write clear technical specs that AI-assisted workflows can execute against

• Use AI tools (Claude Code, Codex, etc.) to move 10x faster on data wrangling, feature engineering, and analysis — while rigorously validating outputs

• Extend our data platform with new sources (third-party APIs, CRB providers, payment rails) when a model needs them

• Process, clean, and verify data integrity — especially for anything that touches lending decisions

• Present findings clearly to non-technical stakeholders; defend recommendations with data