Years of Experience 2 + Years of Experience ML experience is a must — feature engineering, model evaluation, and hands-on application of data-science techniques, not just pipeline plumbing. Working knowledge of statistical record-linkage methods such as the Fellegi–Sunter model: estimating field-level match/non- match weights from labelled data, likelihood-ratio based scoring, and setting evidence-driven decision thresholds. • Proven experience with large-scale ETL: ingesting heterogeneous databases, mapping them to a common data model, and profiling/repairing data quality (placeholders, junk values, inconsistent encodings). • Strong Python (Pandas / PySpark or similar), SQL / Postgres, and Parquet-based pipelines. Fuzzy and phonetic string matching, name normalization, blocking/candidate-generation strategies, and scoring/threshold calibration.