Greetings from SRINAV INC.
Kindly find the below requirement and share your interest.
Position :ML/DQ Scientist
Location : Remote
Duration : Longterm
Brings machine learning capabilities to the DQ programme. Builds anomaly detection, drift monitoring, and pattern-based models to catch data quality issues that rule-based checks miss.
Python / MLflow | Anomaly Detection | Databricks ML | Statistical Drift |
Key Responsibilities
Design and deploy anomaly detection models for numerical, categorical, and time-series data
Implement statistical drift monitoring across pipeline runs and data partitions
Build ML-based completeness prediction and consistency check models
Integrate ML DQ signals into the broader DQ alerting framework
Monitor model performance, retrain on new data patterns, and manage model lifecycle
Document model behaviour and communicate anomaly signals to the DQ team
Requirements
4+ years in data science or ML engineering, with production model experience
Proficient in Python, PySpark, and MLflow on Databricks
Experience with anomaly detection, statistical process control, or data drift frameworks
Familiarity with feature stores and MLOps practices
Ability to explain model outputs to non-technical stakeholders