EXL
Key Responsibilities
Perform deep dive analysis of existing commercial database to identify gaps in Vendor ingestion, data quality, matching and entity resolution Develop sophisticated Fuzzy matching frameworks for Business and Individual entity resolution Build machine learning models to accurately estimate company firmographics (Revenue, SOW etc) Develop ROI based framework to evaluate new vendor data and create robust arbitration logic for existing 3rd party sources Qualifications
Masters degree in Data Science, Statistics, Computer Science, Applied Mathematics, or a related quantitative field. 7+ years of work experience in analytics, data science, or a similar data-driven role. Experience in Entity Resolution/Matching Experience (deduplication, fuzzy matching, record linkage) is a must. Experience with bureau or 3rd Party B2B Data Experience (Use of D&B, Experian, Equifax, ZoomInfo etc.) is a plus Strong proficiency in Python for data analysis, statistical modeling, and machine learning (e.g., pandas, scikit-learn, statsmodels, NumPy). Hands-on experience in developing fuzzy and ML-based entity matching models Candidate demonstrates technical expertise in various text distance matching algorithms (Jaccard, Levenshtein, JW, cosine etc) Strong Python (scikit-learn, pandas, numpy, rapidfuzz) and SQL, familiarity with common tech stacks preferred Strong understanding (and hands-on exp) of ML, ensemble models: parameter tuning, feature engineering, model perf. Analysis etc Seniority level
Mid-Senior level Employment type
Full-time Job function
Business Development Industries
Business Consulting and Services and Data Infrastructure and Analytics
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Perform deep dive analysis of existing commercial database to identify gaps in Vendor ingestion, data quality, matching and entity resolution Develop sophisticated Fuzzy matching frameworks for Business and Individual entity resolution Build machine learning models to accurately estimate company firmographics (Revenue, SOW etc) Develop ROI based framework to evaluate new vendor data and create robust arbitration logic for existing 3rd party sources Qualifications
Masters degree in Data Science, Statistics, Computer Science, Applied Mathematics, or a related quantitative field. 7+ years of work experience in analytics, data science, or a similar data-driven role. Experience in Entity Resolution/Matching Experience (deduplication, fuzzy matching, record linkage) is a must. Experience with bureau or 3rd Party B2B Data Experience (Use of D&B, Experian, Equifax, ZoomInfo etc.) is a plus Strong proficiency in Python for data analysis, statistical modeling, and machine learning (e.g., pandas, scikit-learn, statsmodels, NumPy). Hands-on experience in developing fuzzy and ML-based entity matching models Candidate demonstrates technical expertise in various text distance matching algorithms (Jaccard, Levenshtein, JW, cosine etc) Strong Python (scikit-learn, pandas, numpy, rapidfuzz) and SQL, familiarity with common tech stacks preferred Strong understanding (and hands-on exp) of ML, ensemble models: parameter tuning, feature engineering, model perf. Analysis etc Seniority level
Mid-Senior level Employment type
Full-time Job function
Business Development Industries
Business Consulting and Services and Data Infrastructure and Analytics
#J-18808-Ljbffr