Full Stack Data Scientist
1 week ago
- 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.
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.
- 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.
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