Staff Data Scientist

Job Description

Your Career

As a Staff Data Engineer and Scientist, you will be an integral member of our Customer Analytics team, responsible for shaping the future of our business operations through robust data infrastructure and advanced analytical solutions. This unique hybrid role combines data engineering and applied AI/ML, requiring an entrepreneurial problem-solver who thrives in tackling ambiguous business problems through their deep understanding of the business as well as deep technical expertise. You will act as both a strategic partner as well as builder, developing deep insights , building, developing and curating new datasets, as well as owning the end to end ML/AI model deployment for key customer success initiatives.  

You will be constantly challenged by tough engineering and design tasks, working in a fast-paced setting to deliver high-quality, impactful work.

This is an in office role 3 days/week in our HQ, Santa Clara, CA

Your Impact

In this versatile role, you will drive impact across both data engineering and data science domains:

Data Engineering Foundations

• Design & Development: Design and implement scalable data architectures and datasets that support the organization's evolving data needs, providing the technical foundations for our analytics team and business users. 

• Data Engineering: Support and implement large datasets in batch/real-time analytical solutions leveraging data transformation technologies.

• Data Security & Scalability: Enable robust data-level security features and build scalable solutions to support dynamic cloud environments, including financial considerations.

• Process Improvement: Perform code reviews with peers and make recommendations on how to improve our end-to-end development processes.

AI/ML Innovation & Business Impact

• Develop & Deploy Classical ML Models: Own the end-to-end lifecycle of machine learning projects. You'll build and productionize sophisticated models for critical business areas such as marketing attribution, customer churn prediction, case escalation and other relevant use-cases to post-sales. 

• Optimize AI Agentic Systems: Play a key role in our generative AI initiatives. You will be responsible for characterizing, evaluating, and fine-tuning AI agents—such as conversational systems that allow users to query massive datasets using natural language—to improve their accuracy, efficiency, and reliability.

• Partner with Business Stakeholders: Act as an internal consultant to our Go-to-Market (GTM), Global Customer Services (GCS) and Product and Finance teams. You'll translate business challenges into data science use-cases, identify opportunities for AI-driven solutions, and present your findings in a clear, actionable manner.

• Own the Full Data Science Lifecycle: Your responsibilities will cover the entire project workflow, working with the business to understand the problem, charting a path to solve the problem, feature engineering, model selection and training, robust evaluation, deployment, and, in partnership with the data platform team, ongoing monitoring for performance degradation.

Qualifications

Your Experience 

• 7 plus years experience building and maintain data pipeline both for reporting, analysis and feature engineering.

• Experience building and optimizing clean, well-structured analytical datasets for business and data science use cases. This includes Implementing and supporting Big Data solutions for both batch (scheduled) and real-time (streaming) analytics.

• Prior experience working extensively within dynamic cloud environments, specifically Google Cloud Services (GCS) BigQuery and Vertex AI.

• Prior experience developing dashboards in Tableau/Looker or similar data viz platform.

• Nice to have: Experience implementing and managing data-level security features to ensure data is protected and access is properly controlled.

• Expert-level programming skills in Python and familiarity with core data science and machine learning libraries (e.g., Scikit-learn, Pandas, PyTorch/TensorFlow, XGBoost).

• A solid command of SQL for complex querying and data manipulation.

• Proven ability to work autonomously, navigate ambiguity, and drive projects from concept to completion.

Preferred Qualifications

• Prior working experience in Customer Analytics space and customer experience use-cases, e.g. Escalation, Risk predictors, Renewals and efficiency of project delivery in Professional Services space.

• Direct experience with generative AI, including hands-on work with LLMs and frameworks like LangChain, LlamaIndex, or the Hugging Face ecosystem.

• Experience in evaluating and optimizing the performance of AI systems or agents.

• Demonstrated expertise in specialized modeling domains such as causal inference, time-series analysis. 

• An MS or PhD in a quantitative field like Computer Science, AI, Statistics, or equivalent practical experience or equivalent military experience.