AI Insurance Practice Lead – Strategy & Delivery

EPAM Systems

United States

Remote

USD 180.000 - 280.000

Vollzeit

Vor 5 Tagen
Sei unter den ersten Bewerbenden

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Zusammenfassung

EPAM Systems in the United States is seeking an AI leader to drive innovation and delivery across the Insurance portfolio, overseeing end-to-end AI solutions from ideation to production. You will shape strategy, lead multidisciplinary teams, and build capabilities in a fast-evolving market.

You will manage data science, ML engineering projects, mentor teams, and contribute to offerings. Strong leadership and hands-on experience in NLP, CV, or time-series analytics are preferred; familiarity with

Qualifikationen

  • 10+ years in data science or leadership roles in insurance or related fields.
  • Proven ability to lead cross-functional teams and programs.
  • Strong communications for diverse audiences and stakeholders.

Aufgaben

  • Shape the AI, ML, and agentic vision and go-to-market offerings with clients and internal teams.
  • Manage end-to-end data science and AI projects and programs from ideation to delivery.
  • Lead multidisciplinary workshops and design sessions with customers.
  • Develop high-quality proposals with SMEs and consultants.
  • Contribute to evolving data science and agentic offerings.
  • Grow team capacity, mentor members, and drive transformation with stakeholders.

Kenntnisse

AI leadership
Data science leadership
ML engineering leadership
Cloud platforms
Python
Client-facing
Stakeholder management

Tools

Databricks
Snowflake
LangChain
Semantic Kernel
Omnigent

Jobbeschreibung

EPAM Systems in the United States is seeking an AI leader to drive innovation and delivery across the Insurance portfolio, overseeing end-to-end AI solutions from ideation to production. You will shape strategy, lead multidisciplinary teams, and build capabilities in a fast-evolving market.

You will manage data science, ML engineering projects, mentor teams, and contribute to offerings. Strong leadership and hands-on experience in NLP, CV, or time-series analytics are preferred; familiarity with

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