Data Scientist

Data Scientist (End-to-End Machine Learning, AWS & PySpark)

Job Summary:

We are seeking a skilled Data Scientist to lead end-to-end machine learning (ML) projects on AWS, leveraging services like AWS Glue, SageMaker, and CloudWatch. The ideal candidate will have strong expertise in Python and PySpark, with experience in building, deploying, and monitoring ML models at scale.

Key Responsibilities:

• Design, develop, and deploy ML models on AWS, ensuring scalability and performance.
• Utilize AWS Glue and PySpark to extract, transform, and load (ETL) large-scale datasets.
• Leverage SageMaker for model training, tuning, and deployment, ensuring efficient execution.
• Use CloudWatch and other AWS tools to track model performance, detect anomalies, and optimize pipelines.
• Work closely with data engineers, ML engineers, and business teams to deliver impactful ML solutions.
• Implement best practices for CI/CD in ML workflows, automating model retraining and deployment.

Required Skills & Qualifications:

• 7+ years of experience in data science and machine learning, with a focus on cloud-based solutions.
• Proficiency in Python and PySpark, with hands-on experience in data processing and feature engineering.
• Strong experience with AWS services including AWS Glue, SageMaker, CloudWatch, Lambda, and S3.
• Expertise in ML algorithms, model development, and evaluation techniques.
• Experience in MLOps and automating model deployment pipelines.
• Strong problem-solving skills and ability to work with complex datasets.
• Experience with big data tools (e.g., Spark, Hadoop) is a plus.

Preferred Qualifications:

• Experience with feature stores and model registry solutions.
• Knowledge of Docker and Kubernetes for containerized ML deployments.
• Familiarity with SQL and NoSQL databases.
• Experience with Deep Learning frameworks like TensorFlow or PyTorch is a plus.