We are seeking a highly skilled Data Scientist with specialized expertise in Identity Resolution or Entity Resolution/Matching. The ideal candidate will leverage advanced data science techniques, including machine learning, probabilistic matching, and graph-based algorithms, to resolve and link entities across disparate data sources, ensuring high accuracy in identity and entity disambiguation. This role will involve working closely with cross-functional teams to drive insights, improve data quality, and support business objectives through robust entity resolution solutions. Key Responsibilities • Entity Resolution & Matching: Design, develop, and implement scalable identity and entity resolution algorithms to deduplicate, link, and disambiguate records across structured and unstructured datasets. • Data Analysis & Modeling: Apply statistical and machine learning techniques (e.g., clustering, classification, natural language processing) to analyze complex datasets and improve matching accuracy. • Feature Engineering: Create and optimize features for entity matching, such as name standardization, address parsing, and fuzzy matching techniques. • Graph-Based Solutions: Utilize graph databases and network analysis to model relationships between entities and enhance resolution processes. • Data Quality & Validation: Assess and improve data quality by identifying inconsistencies, missing values, or duplicates, and validate matching results against ground truth or external benchmarks. • Collaboration: Partner with data engineers, product managers, and domain experts to integrate entity resolution pipelines into production systems and align solutions with business needs. • Performance Optimization: Optimize algorithms and workflows for scalability, speed, and accuracy, ensuring they perform efficiently on large-scale datasets. • Documentation & Reporting: Document methodologies, present findings, and provide actionable insights to stakeholders through clear visualizations and reports. Required Qualifications Education: Master’s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field. Experience: • 5+ years of experience in data science or a related role. • 3+ years of hands-on experience with identity resolution, entity resolution, or record linkage projects. Technical Skills: • Proficiency in Python or R for data analysis and modeling (libraries: pandas, scikit-learn, NumPy, etc.). • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and probabilistic matching techniques. • Familiarity with fuzzy matching tools (e.g., dedupe, fuzzywuzzy) and string similarity metrics (e.g., Levenshtein, Jaro-Winkler). • Knowledge of graph databases (e.g., Neo4j) and network analysis for relationship modeling. • Experience with SQL and handling large-scale datasets in relational or No