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!

JD: Data Scientist role on the DFW team

David, June 15

Data Science Engineer, Document Cloud AI team

The Opportunity:

The team’s mission is to use ML to build new experiences with documents, across our entire suite of offerings. Our most recent public features are Liquid Mode , allowing a seamless reflow experience for PDFs phones and tablets; PDF Extract API for unlocking the structure and content elements of any PDF; Table Decomposition feature in Liquid Mode; and now new generative AI features. All products are driven by AI. Our team’s solutions are deployed on mobile devices, in the cloud, and on desktop.

Adobe Document Cloud’s AI team is looking for a data scientist to help us improve our next generation of AI driven features. Our scale is immense, spanning billions of PDFs and many millions of transactions monthly. At its core, this job is about harnessing our data in an ethical way, preserving our user’s privacy while helping to build better products. We are hiring a versatile data scientist to drive analysis, insight and feature extraction of our data, while also contributing to our emerging Data Stack. If you are passionate about data-driven product improvement and want to work with a great group of engineers, scientists, product managers and designers to do great work, we have an exciting opening for you! Please note, this is not a business analytics role, this is entirely focused on product improvement.

What you’ll do:

• Contribute to the overall data architecture, addressing the data needs of the entire Document Cloud business
• Own the effort around data governance, discoverability and access
• Collaborate with Adobe Research and the rest of the organization to architect, build, and launch ML models that understand and join user’s content as well as their behavior data
• Create, publish and maintain our reporting layer in
• Design and implement data pipelines to ETL/ELT data from multiple sources into our data lake.
• Write sophisticated and efficient code to transform raw data sources into easily accessible models by coding across several languages and environments such as SQL, Python, Spark, Databricks, Airflow, Azure, AWS, etc.
• Become the go-to person for data generally within the organization
• Work closely with our Machine Learning Engineers and Researchers to understand what features would provide the best signal, and then make the delivery of these features happen at scale.

What you’ll need to succeed:

• B.S. or M.Sc. or Ph.D. in an analytical field: statistics, applied mathematics, computer science, engineering, economics, physics, etc., or equivalent practical experience
• 5+ years of proficiency in Python and SQL
• 3+ years of expertise with modern data science workflows on the cloud (Git, Jupyter, Python, AWS or Azure)
• Strong proficiency in querying and manipulating large datasets using SQL-like languages (Databricks, Spark-SQL, Presto or similar)
• Strong fundamentals in statistics as well as shown talents in data visualizations
• Ability to support multiple ongoing projects in a fast-paced environment
• Excellent communication and relationship-building skills.
• Expert in one or more data science tools such as Pandas, NumPy, Polars, etc

Nice to have:

• Background in data warehousing, data modeling, data access, and data storage techniques
• Experience crafting dashboards in Tableau, Grafana
• Experience with presentation packages such as Streamlit
• Experience with A/B testing and sampling methods
• Familiarity with ML for propensity estimation and causal inference with observational data or experimental designs