Director, Machine Learning

The Opportunity

At Adobe Experience Platform (AEP), we are experiencing explosive growth in our customer base who ingest petabytes of data encompassing aspects such as experience delivery, user interactions, and conversion metrics. This data is instrumental for Adobe solutions including RTCDP, AJO, and CJA, facilitating the construction of detailed customer profiles, segmentation of sophisticated audiences, orchestration of customized engagement journeys, and the evaluation of business outcomes. The wealth of data available presents a significant opportunity for the development of innovative applications through machine learning, with the potential to substantially enhance the commercial success of our customers. In the capacity of Director of Applied Machine Learning & Data Science, your responsibility will encompass leading initiatives to convert this vast data repository into valuable insights and avant-garde solutions. Your contribution will be critical in advancing the objectives of AEP, employing data to forge impactful, individualized user experiences and to generate measurable commercial benefits for our customers.

Responsibilities

Wherever AEP data is used for building ML based solution, you'll have an important role to play. Your most direct contributions will be in terms of delivering end to end ML solutions (e.g. Lookalike Audiences). In this scenario, you'll be directly responsible for our customers to achieve their business outcomes and you would own all of engineering as well as data science aspects of the ML models.

Additionally, you'll support the machine learning groups in application teams by providing core ML technology and lowering the barrier to high quality data science work for solutions like journey simulation, customer propensity and probabilistic attribution. Also, we want the business results to be the front and center of our machine learning efforts. To that end, you'll closely liaison with the Experimentation group and standardize testing of ML models for all of AEP & Applications.

Finally, you'll also support the machine learning groups within our customer base. These groups have high interest in leveraging the AEP data in their own ML models. To enable these groups, we support an ecosystem of cloud-based machine learning systems. You'll be responsible for maturing our offering in this area.

What you'll do

• Build Data Science foundation through an opinionated and fully integrated system with functionality like automated discovery of important signals, data drift detection and testing of models.

• Provide Generative AI infrastructure that spans training dataset management, LLM fine-tuning and low latency inferencing.

• Consolidate current machine learning solutions into a low cost, configuration driven, general purpose machine learning stack with reusable components.

• Advance AEP's support for cloud-based ML systems so that our customers can export valuable signals from AEP data and bring back model scores to be used in AEP & Applications.

• Articulate important research problems for unlocking the value of AEP data. Partner with Adobe's research organization and help craft a multi horizon roadmap.

• Lead a talented team of wide-ranging skillsets (ML researchers, ML engineers, software engineers and data engineers) to achieve high pace of innovation.

What you need to succeed

• Delivering cloud-scale, data-driven products and services that are widely adopted with large customer bases. Starting with fast-paced MVP development followed by long grind of continuous improvement.

• Leading teams in multiple geographies - one in US and another in Europe or Asia. Hiring and grooming managers. Bridging the skill gaps through training and hiring.

• Advocating for your point of view and sincerely hearing out others. Making decisions in a complex matrix structure with due consideration to diverse opinions.

• Closely working with customers, understanding what's success for them and mapping it to everyday technology choices.

• Communicating complex technical topics in simple terms to internal stakeholders and customers.

• At least 10 years of engineering leadership experience to build high volume data solutions.

• Advanced degree in computer science or equivalent work experience.