Director of Data Science & Engineering

Adobe Adobe · Enterprise · San Jose, CA +1

Director of Data Science & Engineering to build the data foundation, KPI frameworks, and predictive intelligence for Adobe's Experience Platform (AEP) Product Success Engineering team. This role involves leading a multidisciplinary team to architect scalable data systems, develop predictive models, and enable data-driven decision-making, focusing on customer adoption, health, and platform performance.

What you'd actually do

  1. Define and deliver the data science, analytics, and platform strategy for Product Success Engineering.
  2. Build and grow a world-class 20+ person data organization.
  3. Architect end-to-end data pipelines, cloud data warehouse solutions, and real-time and batch analytics.
  4. Develop models for customer health, adoption forecasting, and expansion.
  5. Deliver dashboards and executive reporting that influence product and business strategy.

Skills

Required

  • Data Science
  • Data Engineering
  • People Leadership
  • Predictive Modeling
  • Statistical Methods
  • Model Evaluation
  • Data Strategy
  • Data Architecture
  • Data Pipelines
  • Cloud Data Warehousing
  • ETL/ELT
  • dbt
  • Airflow
  • SQL
  • Python
  • ML Frameworks
  • BI Tools
  • Data Governance
  • B2B SaaS Metrics
  • Product Analytics
  • Customer Lifecycle Insights
  • Executive Communication

Nice to have

  • real-time analytics
  • batch analytics
  • customer-facing insights
  • segmentation
  • funnel insights
  • causal analysis
  • experimentation

What the JD emphasized

  • 10+ years of experience in data science, analytics, or data engineering, including 5+ years in people leadership roles managing teams of 10+
  • 5+ years of hands-on experience in data science and ML, with expertise in predictive modeling, statistical methods, and model evaluation.
  • Proven 0-to-1 data leader with a track record of building data foundations, teams, and analytics capabilities from the ground up.

Other signals

  • Lead a greenfield data organization
  • architect scalable data systems
  • develop predictive models
  • enable data-driven decision-making