Data Science Engineer

Adobe•New York, NY

1d•$154,900 - $281,000

About The Position

Adobe Firefly is redefining how people create, not only inside Adobe's own apps, but increasingly wherever creativity happens. Our generative AI is now reaching users directly through third-party agentic surfaces like ChatGPT, Claude, and Gemini, where people call Adobe's tools without ever opening a traditional Adobe product. Understanding how these experiences are adopted, where they delight, and where they fall short is one of the most exciting and least-charted data problems at the company. We're looking for a Senior Data Science Engineer to help us make sense of it. You'll build the data foundations and generate the insights that shape how our Firefly agent, and the tools it exposes across partner platforms, evolves. You'll partner closely with our Third-Party (3P) team, which owns how Firefly is accessed through external agents and assistants, while staying connected to the broader Firefly data picture. This is a hands-on role for someone who loves turning messy, high-volume, ambiguous data into production-ready pipelines and decisions leadership can act on fast.

Requirements

6+ years of relevant experience in data science, data/ML engineering, or analytics engineering.
A relevant Bachelor's degree and/or advanced degree in a quantitative or engineering field, or equivalent experience; Master's preferred.
Strong skills in at least one programming/data-manipulation language (e.g., Python, SQL, Scala) and the ability to query and integrate data from multiple disparate sources.
Proven experience architecting and maintaining scalable, automated data pipelines and owning large-scale data products across multiple data sources and integrations.
Solid grounding in statistics and experimentation, with the judgment to recognize bias and error and to interpret results in a business context.
Excellent data storytelling, able to synthesize complex analysis for both technical and non-technical audiences.
Comfort with ambiguity and unstructured problems; a self-directed worker who thrives with minimal structure.

Nice To Haves

Exposure to LLM/agentic products, product analytics for AI/ML applications, or usage-data instrumentation for platform or partner ecosystems

Responsibilities

Design and analyze experiments: conduct sound statistical analysis (hypothesis testing, A/B experimentation), recognizing errors and biases and weighing the pros and cons of approaches within the business context.
Turn data into direction: identify patterns and actionable insights that inform product strategy, prioritization, and roadmap decisions for the Firefly agent and its tools across partner platforms.
Be a data steward: develop deep domain intuition for the agentic/3P ecosystem, take on ambiguous business problems, and make well-reasoned assumptions grounded in that understanding.
Tell the story: interpret, simplify, and synthesize results for technical and non-technical stakeholders, and communicate findings that help decision-makers move quickly and confidently.
Own end-to-end data products: Support our data engineering team with the build and maintenance of scalable, automated, and reliable data pipelines that consolidate diverse, high-volume, and often unstructured usage data from across Adobe's agentic and third-party surfaces.
Write production-quality code: apply best practices in coding, testing, and architecture design; review others' code and provide effective feedback; and build tools and processes that benefit multiple teams.
Drive projects to completion: independently manage one or more technical projects start to finish with minimal managerial guidance, and begin to lead initiatives that inform broader team strategy and goals.
Partner cross-functionally: collaborate with Product Management, Engineering, Design, and partner-facing teams to define customer-focused, measurable solutions.
Raise the bar: mentor junior data scientists/engineers, share knowledge across and beyond the immediate team, and help establish best practices; contribute to hiring through technical interviewing.