01Key Responsibilities
Data Science and Machine Learning:
Design, develop, evaluate, and deploy machine learning and advanced analytics solutions for complex business problems. Apply appropriate methods across forecasting, classification, regression, clustering, optimization, and statistical analysis. Establish relevant baselines, evaluation metrics, and validation approaches based on the business objective. Perform exploratory analysis, feature engineering, model selection, error analysis, and performance optimization. Work with structured, semi-structured, and unstructured datasets. Clearly distinguish between correlation, prediction, and causation when interpreting analytical results. Translate model outputs into practical recommendations and measurable business actions.
Production ML and MLOps:
Build reproducible training, validation, and inference pipelines. Collaborate with engineering teams to deploy batch or real-time machine learning solutions. Apply software engineering practices including Git, code reviews, modular development, testing, and documentation. Implement experiment tracking, model versioning, deployment automation, and appropriate CI/CD practices. Define and implement model monitoring, data-quality checks, drift detection, retraining, and performance-management processes. Consider reliability, scalability, latency, cost, security, privacy, and maintainability during solution design. Support model governance, explainability, auditability, and responsible AI requirements.
Cloud Data and Analytics Platforms:
Develop data science and analytics solutions using Azure or comparable cloud platforms. Work with one or more platforms such as Azure Databricks, Microsoft Fabric, Azure Machine Learning, Azure Synapse Analytics, or equivalent technologies. Collaborate with data engineers to define data requirements and support reliable ingestion, transformation, and feature-generation pipelines. Work with large enterprise datasets and implement suitable data-quality and validation controls. Contribute to scalable analytical and machine learning architectures without unnecessarily increasing solution complexity.
Business Analysis and Consulting:
Participate in discovery workshops, requirements discussions, and solution-design sessions. Translate broad business challenges into clearly defined analytical problems. Define success metrics, assumptions, constraints, risks, and acceptance criteria before model development. Evaluate the feasibility and expected value of proposed data science solutions. Challenge the use of machine learning where a simpler analytical or rules-based solution would be more appropriate. Communicate analytical findings, limitations, and .
