Staff Data Scientist

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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.
• 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.
• 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.

Minimum Qualifications:

• 5+ years of Data Science or related work experience.

*Completed advanced degrees in a relevant field may be substituted for up to two years (Master’s = one year, Doctorate = two years) of work experience.

Applicants: Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail [email protected] or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

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