Machine Learning & Data Science Engineer

Job Description

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.