About the role
Area(s) of responsibility
Location: Hyderabad / Bangalore / Pune / Noida (Hybrid)
Experience: 5–12 Years
Employment Type: Full-Time
Role Overview 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.
Key Responsibilities
- 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.
Required Skills & Qualifications
- 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.
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