Full Stack Data Scientist

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

• 4 to 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.