Perplexity20 дней назад

Специалист по данным (AI Builder)

Зарплата не указана
РЫНОК
15 450медиана по профессии
Data Scientist · 36 вакансий с указанной зарплатой
7 333половина предложений: 12 500–20 277150 000
Работодатель не указал зарплату — сравните с рынком сами.
Полная занятостьSan Francisco

Обязанности

  • 01Build AI agents that do data science - not just SQL copilots, but systems that can safely explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations with clear evaluation and human review loops
  • 02Make AI systems query the warehouse reliably - build the retrieval infrastructure and evaluation loops that let agents use our semantic context and metadata accurately
  • 03Accelerate the AI-native data workflow - turn the best existing AI-assisted workflows into repeatable systems, reusable tools, and patterns the whole data team can adopt
  • 04Automate the data lifecycle - build self-healing pipelines, automated dbt model generation and validation, data quality agents, and diagnosis workflows that reduce manual firefighting
  • 05Ship AI-powered experiment analysis - build agents that interpret A/B test results, flag statistical issues, identify likely drivers, and draft ship/no-ship recommendations
  • 06Turn the data team into a product team - build internal data products that stakeholders use every day, replacing ad hoc requests with self-serve AI interfaces
  • 07Own the full lifecycle - identify high-leverage problems, prototype with LLMs, evaluate accuracy, design the UX, ship to production, and monitor quality over time

Требования

  • 016+ years in data science, analytics engineering, data engineering, or a related role
  • 02Deep SQL and analytics judgment
  • 03Strong product sense
  • 04Production-oriented Python ability
  • 05Hands-on LLM experience
  • 06Pipeline and modeling fluency
  • 07Builder mentality
  • 08Autonomy

Условия

  • 01Set the standard for the industry
  • 02Build AI with AI
  • 03Frontier models, day one
  • 04Massive leverage
  • 05Direct impact
  • 06Small team, no layers of approval
  • 07Idea to shipped system in days, not quarters