HYDERABAD · FULLTIME
Staff Data Scientist
Qualcomm
Hyderabad · onsite · Posted 57d ago
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Undisclosed10–15 yrsfulltimeHyderabad
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Section · 01
About this role
Job 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 deliveryfrom data preparation and modeling to deployment, monitoring, and user facing experiences.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field.
- 5+ years of Data Science or related work experience.
- Completed advanced degrees in a relevant field may be substituted for up to two years (Masters = one year, Doctorate = two years) of work experience.
- Familiarity with Lakehouse architectures, feature stores, and ML lifecycle management.
- Experience with MLOps practices, CI/CD, model monitoring, and retraining pipelines.
- Exposure to cloud platforms (e.g., AWS) and scalable ML infrastructure.
- Experience embedding ML and agent outputs into enterprise applications or analytics platforms.
- Knowledge of data governance, access controls, and secure ML deployment.
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Section · 02
Skills
AutomationData ingestioncloud platformsapisdata preparationclusteringamazon web servicesclassificationanalytics platformsAnalyticsAnomaly DetectionData EngineeringcloudData GovernanceApplication DevelopmentData ProcessingArtificial Intelligence
Section · Company
About Qualcomm
Qualcomm
Semiconductors
22k+
employees
1996
30 years old
San Diego,California
United States
About
Qualcomm invents breakthrough technologies that transform how the world connects, computes and communicates.
Industries
SemiconductorsHardware & Networking
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1,207 reviews
3.7/ 5
Culture
3.4
Career growth
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Work-life
3.3
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