Back
Popular Data Scientist title but senior and specialized role reduces candidate pool, yielding medium competition.
Specialized ML and entity-resolution requirements limit transferability.
Explicit 10+ years requirement and specific ML/entity-resolution/production skills cause high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own and manage the complete machine learning model lifecycle including problem framing, data exploration, feature engineering, model training, deployment, and monitoring.
Build and maintain entity resolution systems and multi-class classification models to improve data quality and support business decision-making.
Develop and optimize feature engineering techniques and indexing strategies to ensure model performance at scale, and maintain model and data pipeline currency in production.
10+ years of experience in end-to-end ML model development and deployment.
Strong proficiency in Python and ML libraries such as pandas, NumPy, scikit-learn, and XGBoost or similar.
Experience specifically with record linkage, entity resolution, deduplication, and classification model building.
Proficiency with string similarity algorithms and SQL databases (e.g., PostgreSQL).
Experienced in handling large, noisy, and semi-structured text data with strong feature engineering skills.
Familiar with balancing precision and recall in production models and tuning model thresholds accordingly.
Capable of collaborating with cross-functional teams to translate business needs into precise ML solutions focused on data quality and classification tasks.