Key Skills: Pyspark, Fast Api, Numpy, SQL, Pandas, Python, Apache Spark, Databricks, GitHub, Pytorch, Snowflake

Roles and Responsibilities:

• Develop and deploy predictive and prescriptive models using statistical and machine learning techniques.
• Analyze complex datasets to extract actionable insights and enable data-driven decision-making.
• Design, train, validate, and optimize models for classification, regression, clustering, and forecasting tasks.
• Build and maintain scalable ML pipelines for model training, evaluation, and production deployment.
• Collaborate with cross-functional teams to define use cases, gather data requirements, and interpret model outputs.
• Implement model monitoring, versioning, and retraining strategies to ensure sustained performance and reliability.
• Apply MLOps practices to streamline model lifecycle management, including CI/CD workflows for ML.
• Leverage Azure ML, AWS SageMaker, and GCP Vertex AI for scalable experimentation and deployment.
• Document methodologies, assumptions, and results to ensure transparency and reproducibility.

Skills Required:

• Strong proficiency in Python for data manipulation, model development, and automation.
• Hands-on experience with machine learning libraries including Scikit-learn, TensorFlow, PyTorch, and XGBoost.
• Expertise in data analysis libraries such as NumPy, Pandas, SciPy, and Matplotlib.
• Experience working with PySpark and Databricks for large-scale data processing.
• Proficiency in SQL for data querying and transformation.
• Experience developing APIs and lightweight applications using FastAPI, Dash, or Streamlit.
• Familiarity with CI/CD practices using Jenkins and GitHub Actions.
• Experience with cloud ML platforms such as Azure ML, AWS SageMaker, and GCP Vertex AI.
• Understanding of Apache Spark and Snowflake for distributed data processing and storage.
• Familiarity with tools such as Jupyter Notebook, Dataiku, or MATLAB is advantageous.
• Experience in model monitoring, production support, and solution sustainment for operational ML environments is valuable.
• Strong analytical thinking, communication, and stakeholder management skills.

Education: Bachelor's or Master's degree in Computer Science