Description

Own the end-to-end lifecycle of data science solutions, from statistical modeling and deep learning to cloud deployment and MLOps.

This role is on-site.

Responsibilities

• Build and deploy machine learning models using Python, Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
• Perform complex data extraction and manipulation using SQL across PostgreSQL, Oracle, and MS SQL Server.
• Apply statistical modeling techniques, including time-series forecasting and anomaly detection, to large datasets.
• Process big data using distributed computing frameworks like Apache Spark.
• Deploy and manage models on AWS, GCP, or Azure, utilizing native ML services and MLOps tools such as MLflow and Docker.

Required Skills

• Expert-level proficiency in Python and core data science libraries.
• Strong SQL skills for complex queries and data manipulation.
• Hands-on experience with deep learning frameworks (TensorFlow or PyTorch).
• Proven experience in statistical modeling, time-series forecasting, and anomaly detection.
• Experience working with large datasets using Apache Spark.
• Experience with relational databases (PostgreSQL, Oracle, MS SQL Server).
• Experience building and deploying solutions on AWS, GCP, or Azure.
• Practical knowledge of MLOps principles, model versioning, and containerization (Docker).
• 8+ years of experience in data science or machine learning roles.

Preferred Skills

• Familiarity with cloud-native ML services (AWS SageMaker, Google Vertex AI).

Key Skills

Education

Bachelor's degree