Design, develop, and maintain scalable data pipelines using PySpark to efficiently process large volumes of data.
Develop, test, and optimize Machine Learning models for predictive analytics, including regression, classification, and clustering techniques.
Manage and deploy Machine Learning environments and workflows using AWS SageMaker, while leveraging Athena for data querying and analysis.
Integrate Machine Learning models into production pipelines, ensuring proper monitoring, maintenance, and continuous improvement through MLOps best practices.
Collaborate closely with cross-functional teams to understand business challenges and translate them into data-driven solutions.
Conduct advanced data analysis using Python and SQL to deliver actionable insights.
Work collaboratively with multidisciplinary teams using Git/GitHub and agile development methodologies.

2–3 years of experience in Data Engineering, Machine Learning, or similar roles.
Advanced proficiency in Python, including libraries such as Pandas, NumPy, and Scikit-learn.
Strong SQL skills for data querying, transformation, and analysis.
Hands-on experience with PySpark for large-scale data processing.
Solid understanding of Machine Learning algorithms, including regression, classification, clustering, and hyperparameter optimization techniques.
Experience working with AWS services, especially SageMaker and Athena.
Knowledge of MLOps practices and experience integrating and monitoring ML models in production environments.
Familiarity with Git and collaborative development workflows using GitHub.
Strong analytical thinking and problem-solving skills.
Curiosity, autonomy, attention to detail, and a collaborative mindset.

Data Scientist / ML Engineer

Key skills