Data Scientist with QuickSight Knowledge

Role: Data Scientist with QuickSight Knowledge Location: Santa Clara, California -Hybrid: Yes Mandatory skills: SQL, Python, AWS Bedrock, Redshift, Agent Core, QuickSight, statistical analysis, Lang Graph, Agentic AI, Generative AI. Experience 12+ Years in Data Science /ML Job Description • Become a domain expert in the company's customer support operations and platform data, developing a strong understanding of how the business operates and where value is created. • Translate ambiguous business questions into clear, actionable analyses and solid deliverables; define metrics that measure customer support effectiveness and operational performance. • Build and maintain dashboards that surface key insights for stakeholders across support and analytics functions. • Design and apply statistical methods to evaluate program effectiveness, support operational decisions, and identify trends in customer and user behavior. • Leverage LLM- and agent-based approaches to automate recurring analyses, accelerate data exploration, and build self-serve tools that let stakeholders query and interpret data without manual intervention. • Produce ad-hoc analyses and reports that help teams make time-sensitive decisions with confidence. • Communicate findings clearly to both technical and non-technical partners; distill complex data into concise, actionable insights. Roles & Responsibilities • Experience in Analytics, Data Science, Statistics, Mathematics, or equivalent industry experience. • Atleast 3 years of experience as a data scientist or data analyst in a data-driven environment. • Strong SQL skills with the ability to write and optimize complex queries. • Working proficiency in Python for data analysis and automation. • Experience building dashboards and data visualizations (AWS QuickSight preferred). • Background in statistical design and analysis (e.g., experiment design, hypothesis testing, regression). • Ability to take mid-to-complex tasks from ambiguity through to delivered results with minimal hand-holding. • Strong communication skills with a track record of creating insights that influence decisions. • Some experience working within AWS environments (e.g., S3, Redshift). • Hands-on experience with LLMs and agentic AI concepts - prompt engineering, retrieval-augmented generation (RAG), and building or integrating agent workflows (e.g., tool use / function calling, orchestration frameworks such as LangGraph, or protocols like MCP). • Familiarity with applying agentic patterns to real analytics use cases (e.g., text-to-SQL, automated report generation, or conversational data assistants) is a strong plus.