Lead Data Scientist / Machine Learning Engineer

Our Company

Changing the world through digital experiences is what Adobe's all about. We give everyone-from emerging artists to global brands-everything they need to design and deliver exceptional digital experiences! We're passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen.

We're on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

We are looking for a Lead Data Scientist / Machine Learning Engineer to build and deploy impactful models and lead the development of the infrastructure that will enable scalable, efficient, and reproducible data science work across our organization.

In this role, you will design and deploy advanced statistical and machine learning models that help us understand and optimize our business, with particular emphasis on causal inference, media optimization, and incrementality measurement. Your work will help the company shift from a correlation-based approach to one that emphasizes causal impact and true business value.

Beyond modeling, you will play a critical leadership role in shaping the tools, systems, and workflows that enable data scientists to do their best work. We're especially excited about candidates who are motivated by innovation and new technologies - someone who proactively explores ways to use large language models (LLMs), automation, and AI agents to make DS and analytics workflows more efficient, discoverable, and impactful.

• Develop and deploy statistical and machine learning models to drive business decisions.
• Modernize data science workflows: Help move teams from manual development (e.g., working with spreadsheets or local code) to streamlined, automated, and collaborative workflows that support experimentation, versioning, and deployment.
• Partner with DS teams to operationalize models: Help data scientists translate research code into robust, production-grade systems, defining processes for feature generation, deployment, and monitoring.
• Partner with engineering: Design systems for scalable deployment, monitoring, and retraining of DS models.
• Modernize DS workflows: Architect tools and frameworks that support collaborative, reproducible, and discoverable data science work across multiple teams.
• Make insights and outputs accessible and reusable. Solve the problem of lost or siloed knowledge by designing ways to capture and organize models, analyses, and findings so that others can find and build on past work.
• Act as a connector across teams: Bridge gaps between data science, analytics, and engineering, ensuring that infrastructure investments serve near-term modeling needs and long-term scalability.

• MS/PhD or equivalent experience in Computer Science, Statistics, Data Science, Economics, Mathematics, or a related quantitative field.
• Familiarity with experiment design, quasi-experimental methods, or causal modeling techniques.
• Familiarity with traditional ML methods.
• Experience partnering with engineering teams to build production systems.
• Track record of deploying ML models into production, including monitoring, retraining workflows, and data quality pipelines.
• Experience applying LLMs, generative AI, or automation frameworks to improve data science efficiency is a plus.
• Can influence and align diverse users without formal authority.
• Can understand, reimagine, and connect the dots across messy, end-to-end DS/analytics processes.
• Comfortable leading through ambiguity, turning loosely defined goals into concrete proposals and roadmaps.
• Proficiency in Python/R, cloud platforms (e.g., Azure), and tools like MLflow for managing model lifecycles.
• Brings hands-on experience with CI/CD practices for machine learning production workflows.
• Communicates concisely and with situational awareness.
• A curious, motivated approach with a passion for innovation and using emerging technologies.

At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

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California:

Fair Chance Ordinances

Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances.

Colorado:

Application Window Notice

If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.

Massachusetts:

Massachusetts Legal Notice

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

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