Associate Data Scientist

We are seeking a highly skilled Data Scientist with strong expertise in Artificial Intelligence (AI), Machine Learning (ML), and MLOps. The ideal candidate will be responsible for building scalable predictive models, driving advanced analytics, and operationalizing ML models in production environments. This role requires a deep understanding of statistical modeling, predictive analytics, and Python-based data ecosystems, with exposure to modern platforms such as Databricks Mosaic AI and Snowflake Cortex being an added advantage., * Design, develop, and deploy machine learning and AI models for real-world business problems.

• Perform advanced statistical analysis and build predictive models to derive actionable insights.
• Develop and implement end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, and deployment.
• Build and manage MLOps frameworks for continuous integration, delivery, monitoring, and model governance.
• Work closely with data engineering teams to ensure robust and scalable data pipelines.
• Conduct exploratory data analysis (EDA) and hypothesis testing to support data-driven decision-making.
• Optimize model performance through hyperparameter tuning and advanced techniques.
• Deploy and monitor models in production environments ensuring performance, reliability, and scalability.
• Collaborate with cross-functional teams including business stakeholders, architects, and product owners.
• Stay updated with the latest advancements in AI/ML, GenAI, and data science tools and frameworks.

• Strong programming expertise in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch).
• Hands-on experience in Machine Learning & AI algorithms (supervised, unsupervised, deep learning).
• Expertise in statistical analysis, hypothesis testing, regression, classification, clustering, and forecasting models.
• Experience with predictive modeling and advanced analytics techniques.
• Solid understanding of MLOps practices including CI/CD pipelines, model versioning, monitoring, and deployment.
• Experience working with large-scale datasets and distributed computing frameworks.
• Strong knowledge of SQL and data manipulation techniques.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.

Good to Have

• Exposure to Databricks (Mosaic AI, MLflow, Delta Lake).
• Experience with Snowflake Cortex / Snowflake ML capabilities.
• Understanding of Generative AI / LLM-based applications.
• Experience in model explainability, fairness, and governance frameworks.
• Knowledge of containerization tools like Docker and orchestration tools like Kubernetes.