Machine Learning Engineer

Tasks

• Analyze data structure and assess data quality
• Apply normalization and masking for privacy
• Build data parsing and entity extraction pipelines
• Build lookalike audience logic
• Develop ML models for anomaly and pattern detection
• Develop probabilistic models for demographic and behavioral attributes
• Evaluate and calibrate model performance
• Identify data quality issues and propose improvement mechanisms
• Perform feature engineering and model selection
• Size and update audience segments
• Write results to feature store

Perks/Benefits

• Advanced English language skills
• Startup experience

Skills/Tech-stack

Anomaly Detection | Apache Airflow | Big Data | Data masking | Databricks | Deep learning | Docker | ETL | Embedding | Feature Engineering | Feature Store | Fraud Detection | GRPC | Gradient boosting | Kubeflow | Lookalike modeling | Machine Learning | Model Interpretability | Model calibration | Nearest neighbors | NumPy | PII audit | Pandas | Pattern detection | Privacy Compliance | PyTorch | Python | REST API | Recommender Systems | SHAP | SQL | Scikit-learn | Segmentation | Spark | TensorFlow