Data Scientist

Key Responsibilities

• Translate business problems into Machine Learning problems, selecting appropriate modeling approaches and metrics.
• Prepare, split, and label datasets to support robust model training and evaluation.
• Define and compute features from multiple data sources.
• Design, execute, and evaluate experiments to assess model performance.
• Build, deploy, and maintain Machine Learning models in production using MLOps best practices.
• Define and monitor model performance and drift metrics, triggering model retraining when required.
• Collaborate closely with engineering and business stakeholders to ensure scalable and reliable solutions.

Required Skills & Experience

• 2+ years of experience building and deploying Machine Learning models in production.
• Hands-on experience with MLOps setups, including CI/CD pipelines.
• Strong experience in time series–based event detection and classification.
• Solid background in classification models.
• Experience modeling geospatial time series data.
• Knowledge of 2D interpolation and extrapolation methods.
• Strong fundamentals in trigonometry and linear algebra (vector arithmetic).
• Proficiency in Python.

Technical Stack

• Python and PySpark
• Spark
• Git
• Azure Pipelines
• Azure Databricks
• Azure-based cloud environments