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.
