Overview

We are looking for an end-to-end Data Scientist to design, build, and maintain ML-powered systems that solve core data quality and classification problems across the business. You will own the full lifecycle — from exploratory analysis and feature engineering through model training, deployment, and ongoing performance monitoring. The work spans entity resolution (identifying duplicate records across large datasets) and multi-class classification models that drive decision-making across a variety of business domains.

Responsibilities

What You'll Do

Own the end-to-end model lifecycle: problem framing, data exploration, feature engineering, model training, evaluation, deployment, and monitoring
Build and maintain entity resolution systems that detect duplicate records using supervised ML and string similarity techniques
Develop classification models that categorize unstructured or semi-structured data into meaningful business categories
Engineer features from messy, real-world text data — names, addresses, free-text fields — using string matching algorithms,



phonetic encoding, n-grams, and other NLP techniques
Design candidate retrieval and indexing strategies to make models performant at scale
Tune thresholds, scoring logic, and rule-based overrides to balance precision and recall for production use cases
Maintain production model artifacts and data pipelines, ensuring models stay current as underlying data evolves
Collaborate with engineering and product teams to understand requirements and translate business problems into well-scoped modeling tasks

Qualifications
10+ years of experience building and deploying ML models end-to-end (not just notebooks)
Robust Python skills — pandas, NumPy, scikit-learn, XGBoost or similar gradient boosting frameworks
Hands-on experience with record linkage, entity resolution, or deduplication problems
Experience building classification models (binary and multi-class) on structured and semi-structure

📌 Senior Data Scientist Hitec City
🏢 RealPage
📍 HITEC City