Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
Specialization
Data Science Advanced: Data Specialist
Job requirements
Key Responsibilities
Design, implement, and manage scalable machine learning (ML) pipelines using Azure ML, Databricks, and PySpark.
Build and maintain automated CI/CD pipelines with Github and Github Action, incorporating SonarQube to ensure code quality and security standards.
Utilize Azure Kubernetes Service (AKS) to containerize and deploy machine learning models, ensuring high availability and scalability.
Have understanding of over all architecture and can work on scalable solutions
Develop reusable templates for various ML use cases to streamline the model deployment process and enhance operational efficiency.
Design and manage APIs to facilitate seamless interaction between ML models and other applications, ensuring robust, secure, and scalable API interfaces.
Perform model optimization, monitor data drift, data refresh checks, and ensure the ML pipelines are cost-efficient.
Implement cost monitoring and management strategies to ensure efficient use of resources, particularly for model training and deployment phases.
Work closely with data scientists, DevOps, and IT teams to deploy and manage machine learning models across environments.
Provide thorough documentation for ML workflows, pipeline templates, and optimization strategies to support cross-team collaboration.
