Sr. Director, AI, Data Science & Engineering

Adobe•San Jose, CA

1d•$211,600 - $436,975

About The Position

We are seeking a Senior Director of AI, Data Science & Engineering to lead the technical organization responsible for building the next generation of AI-powered fraud, abuse, Trust & Safety, and consumer protection capabilities. This leader will oversee AI Engineering, Data Science and Machine Learning, and Data Engineering, with responsibility for taking complex trust and risk problems from concept through production. This is an AI-first leadership role requiring deep AI/ML expertise, strong engineering judgment, and the ability to build and scale multidisciplinary technical teams. This is not a traditional analytics or data science leadership role. We are looking for a technical leader who can connect AI, machine learning, data, and engineering into scalable systems and lead the organization responsible for building them. The Senior Director will define the technical strategy for applying AI across Consumer Trust, with a focus on agentic systems, machine learning, graph intelligence, anomaly detection, behavioral modeling, automation, and real-time risk decisioning. The role will also partner closely with Platform Risk and engineering teams responsible for capabilities such as browser and device fingerprinting, identity graph, bot detection, telemetry, risk signals, and shared decisioning infrastructure. A key responsibility will be connecting these capabilities with AI, ML, and data to create integrated end-to-end protection systems.

Requirements

Significant experience building and deploying AI and machine learning systems in production at scale.
Strong technical background in modern AI/ML, including generative AI, large language models, agentic systems, model evaluation, and traditional machine learning.
Demonstrated experience leading multidisciplinary organizations across AI/ML, data science, and data engineering.
Strong production engineering and systems architecture experience, with the ability to take AI capabilities from prototype through production.
Experience with large-scale data platforms, telemetry, real-time systems, feature pipelines, and ML infrastructure.
Strong understanding of distributed systems and the engineering requirements associated with high-volume, low-latency decisioning.
Ability to engage deeply with principal engineers, AI/ML engineers, data scientists, data engineers, and architects on technical strategy and design.
Strong executive communication skills and the ability to influence across engineering, product, operations, policy, and senior leadership.
Experience in one or more areas such as fraud prevention, cybersecurity, identity, platform abuse, Trust & Safety, payments risk, bot detection, or other adversarial environments.

Nice To Haves

Experience with identity resolution, identity graphs, graph ML, browser or device intelligence, behavioral signals, reputation systems, or real-time risk platforms is strongly preferred.
Experience deploying generative AI or agentic systems into high-impact production environments is also highly desirable.

Responsibilities

Lead AI Engineering, Data Science/Machine Learning, and Data Engineering across Consumer Trust.
Define and execute the AI and ML technical strategy for fraud, abuse, Trust & Safety, and consumer protection.
Own end-to-end development of AI and ML capabilities from experimentation and modeling through engineering, deployment, monitoring, and production operation.
Drive development of agentic AI, graph intelligence, anomaly detection, behavioral models, and real-time decisioning capabilities.
Build the data, feature, evaluation, and ML infrastructure required to operate AI systems at scale.
Partner with Platform Risk and engineering teams on identity graph, browser and device fingerprinting, bot detection, telemetry, and shared risk platforms.
Bring together platform signals, identity intelligence, AI, ML, and data into cohesive detection and decisioning systems.
Establish strong engineering standards for AI systems, including evaluation, reliability, observability, scalability, and responsible AI.
Recruit, develop, and lead strong AI, ML, data science, data engineering, and technical leadership talent.
Translate technical investments into measurable improvements in fraud prevention, consumer protection, operational efficiency, and business outcomes.