Staff Data Scientist

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Company:

Qualcomm India Private Limited

Job Area:

Information Technology Group, Information Technology Group > Data Science

General Summary:

The Staff Data Scientist / Full Stack Developer is a senior, hands on individual contributor responsible for designing, building, and operationalizing traditional machine learning models, agentic AI systems, and Databricks native data applications that drive real business outcomes. This role operates at the intersection of data science, ML engineering, and full stack data application development, with a strong focus on production grade solutions. This position requires deep expertise with AI assisted development of classic ML techniques, agent based AI workflows, and Databricks application development, along with strong ownership of end to end delivery—from data preparation and modeling to deployment, monitoring, and user facing experiences.

Key Responsibilities
Traditional Machine Learning & Analytics

• Design, develop, and deploy traditional machine learning models, including regression, classification, clustering, time series forecasting, and anomaly detection.
• Perform feature engineering, model selection, training, validation, and performance tuning on large scale enterprise datasets.
• Apply sound statistical and ML best practices to ensure model robustness, explainability, and business relevance.

Agentic AI & Intelligent Automation

• Design and implement agentic AI workflows, where autonomous or semi autonomous agents orchestrate data access, ML inference, decision logic, and actions.
• Build multi step agent pipelines that combine rules, ML models, and reasoning components to solve complex business problems.
• Integrate agentic systems with enterprise data, ML models, and applications to enable intelligent automation and decision support.

Databricks Application Development

• Design and develop Databricks native applications, including notebook based apps, interactive dashboards, and parameterized data/ML workflows.
• Build data and ML services/APIs leveraging Databricks, Python, and Lakehouse capabilities.
• Partner with analytics, BI, and application teams to embed ML insights, predictions, and agent outputs directly into Databricks apps and business workflows.
• Ensure Databricks apps meet performance, security, governance, and usability standards.

ML Engineering & Productionization

• Operationalize ML models and agentic workflows into production pipelines, ensuring scalability, reliability, and monitoring.
• Collaborate with data engineering teams to leverage curated Lakehouse data, feature stores, and governed datasets.