Build AI agents and infrastructure that autonomously conduct data analyses, make warehouses AI-readable, and create self-healing pipelines to 10x data team productivity at an AI company. Requires 6-8+ years in data science/analytics engineering, deep SQL and Python skills, product sense, and hands-on LLM/AI experience.
175k – 330k/yr
On-site6+ YOEData Science
About the role
What You'll Do
Accelerate the AI-native data workflow by turning existing practices into repeatable systems, scalable tools, and patterns for the data team and company.
Build AI agents that conduct end-to-end data science analyses: explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations.
Make the data warehouse AI-readable by building semantic layers, context, and retrieval infrastructure for accurate querying by any AI system.
Automate the data lifecycle with self-healing pipelines, automated dbt model generation and validation, and data quality agents.
Ship AI-powered experiment analysis tools that interpret A/B test results, flag statistical issues, and draft recommendations.
Own the full lifecycle from problem identification to prototyping with LLMs, iteration, production deployment, and monitoring.
Turn the data team into a product team by building internal data products and self-serve AI interfaces.
What We're Looking For
6-8+ years in data science, analytics engineering, or related role.
Strong product sense with experience working with product and business teams.
Deep SQL expertise, including building data models and working with warehouses.
Pipeline experience with dbt and handling data quality issues.
Software engineering skills in Python to build and ship tools, wrangle APIs, and deploy services.
Genuine excitement about AI, with hands-on experience building with LLMs, agents, RAG systems, or AI workflows.
Builder mentality focused on automating manual processes and shipping fast.
Autonomy to define roadmap in a new function.
Nice-to-Haves
Experience with dbt for production models.
Snowflake administration and optimization.
Built Slack bots, internal CLI tools, or developer productivity tools.
Background in AI agent frameworks.
Experience with BI tools.
A/B testing and experimentation design and analysis.
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