- End-to-End Model Development

- Design, train, and deploy machine learning models (supervised/unsupervised learning, NLP, time-series forecasting) using Python (scikit-learn, TensorFlow, PyTorch) and R for specialized statistical analysis.

- Optimize models for computational efficiency via parallelization, quantization, and cloud-native architectures (AWS SageMaker, GCP Vertex AI).

- Data Engineering & Pipeline Automation

- Build robust ETL/ELT pipelines for structured/unstructured data using SQL, Spark, and Apache Airflow.

- Implement feature engineering workflows and ensure data quality for large-scale datasets (1M+ records).

- Cross-Functional Collaboration

- Partner with product, risk, and engineering teams to operationalize models into business applications (e.g., credit risk systems,



customer segmentation tools).

- Mentor junior engineers and lead code reviews to maintain best practices in MLops workflows.

- Model Governance & Compliance

- Develop monitoring frameworks for model drift, bias detection, and performance degradation.

- Ensure compliance with regulatory standards and internal governance policies.

- Innovation & Research

- Stay ahead of industry trends (e.g., LLMs, generative AI) and prototype solutions using no-code platforms.

- Publish internal white papers on novel methodologies and present findings to stakeholders.

📌 Machine Learning & Data Science Engineer (Mumbai)
🏢 ICRA Analytics
📍 Mumbai