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.
- Implement model monitoring, drift detection, and retraining strategies to maintain long term model effectiveness.
- Full Stack Data Enablement
- Develop end to end solutions that span data ingestion, modeling, ML inference, agent execution, and user facing applications.
- Translate business and analytical requirements into scalable, maintainable ML powered data products.
- Enable downstream consumption through Databricks apps, dashboards, APIs, and integrated enterprise applications.
- Production Support & Operational Excellence
- Own production ML models, agentic systems, and Databricks applications, including monitoring, troubleshooting, and root cause analysis.
- Implement logging, alerting, and observability for models, agents, and applications.
- Drive continuous improvements in model accuracy, system reliability, and user experience.
- Technical Leadership & Influence
- Serve as a technical authority in traditional ML, agentic AI, and Databricks application patterns.
- Influence architectural decisions, best practices, and technical standards across teams.
- Mentor peers and raise the bar on ML rigor, engineering quality, and production readiness.
Show the full description
- 10+ years of hands on experience in data science, applied machine learning, or ML engineering, with ownership of production systems.
- 5+ years of experience in building RAG based GenAI agentic applications and workflows
- Strong proficiency in Python for ML development, data processing, and application logic.
- Deep experience with traditional ML techniques (e.g., regression, classification, clustering, time series).
- Proven experience building and deploying ML models in production environments.
- Hands on experience with Databricks, including Databricks application development (notebooks, workflows, dashboards, ML pipelines).
- Strong understanding of feature engineering, model evaluation, and explainability.
- Experience collaborating with data engineering, BI, and application teams.
- Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related field.
- Familiarity with Lakehouse architectures, feature stores, and ML lifecycle management.
- Experience with MLOps practices, CI/CD, model monitoring, and retraining pipelines.
