6 - 8 Years of Experience · Database, SQL, Python, DataRobot, Snowflake, GitHub · BAU Tasks & Model Retraining for Machine Learning Projects · Banking domain expRetrain and calibrate governed predictive models for AML compliance, fraud, and core FS use cases to improve precision/recall and reduce false positives. Relevant work experience in fintech fraud risk, with deep understanding of money movement products, banking, lending, and fraud detection data
2 Deliver customer segmentation, anomaly detection, and forecasting solutions by applying hands-on ML expertise to ship production-ready models in financial services. Leverage experience across credit risk and fraud to design, deploy, monitor, and maintain models (deep learning, tree-based, reinforcement learning, clustering, time series, causal methods, and NLP) with a deep understanding of payment systems, money movement, banking, and lending.
3 Continuously monitor model performance, drift, and stability; define thresholds and trigger retraining with clear acceptance criteria.
4 Partner with ML Engineering to provide L2/L3 production support—triage incidents, perform root-cause analysis, and implement hotfixes within MLOps guardrails.
5 Engineer features and prepare training/eval datasets with Data Engineering; contribute to the design and rollout of a reusable feature store
6 Document models, assumptions, and controls; perform structured handoffs to MLOps/ML Engineering for compliant deployment.
7 Write production-quality code for DS workflows (SQL, Python); use advanced Excel for analysis/reporting; enforce testing and reproducibility.
8 Ability to quickly develop a deep statistical understanding of large, complex datasets
9 Expertise in designing and building efficient and reusable data pipelines and framework for machine learning models
10 Strong business problem solving, communication and collaboration skills
