Job
Description
Key Responsibility: 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. Technical Requirements Core Skills: Python (expert), R (intermediate), SQL, Apache Spark, Apache Arrow, Pydantic Machine Learning: Regression, classification, clustering, deep learning (CNNs, RNNs) Frameworks: scikit-learn, PyTorch, TensorFlow, Hugging Face, MLflow Cloud: AWS/GCP/Azure or any cloud platform, Docker, Kubernetes Domain Expertise (Preferred): Financial services (PD/LGD modelling, ECL calculations, risk analytics) Experience with no-code platforms is a bonus Knowledge of regulatory frameworks (Basel III, IFRS 9) Soft Skills & Culture Fit Ownership Mindset: Take end-to-end responsibility for projects, from ideation to deployment. Collaboration: Excel in cross-functional teams with clear communication to technical and non-technical stakeholders. Innovation: Continuously explore emerging tools (e.g., LLMs, AutoML) to solve business challenges.
