The Opportunity
Adobe is seeking Product Analytics Experts passionate about data collection, AI/ML, problem-solving, and data-driven decision-making. If youre excited to uncover hidden data trends, optimize user experiences, and drive growth for Adobes Experience Cloud products, this is your opportunity to make a significant impact.
Responsibilities: Data Collection/Telemetry: Lead data collection efforts using tools like Adobe WebSDK and Adobe Launch, ensuring accurate tracking of user interactions and product performance. Collaborate with engineering teams to ensure smooth data flow and integrity. AI/ML Application: Leverage AI and machine learning algorithms to automate data collection workflows, data validation of telemetry, predict trends, uncover insights, and optimize product features. Product Optimization: Provide data-driven recommendations to optimize Adobe s Experience Cloud products, applying AI/ML models for continuous improvement. Cross-Functional Collaboration: Work with product, engineering, and marketing teams to turn business needs into data-driven solutions. End-to-End Project Ownership: Independently manage analytics projects, ensuring that data insights are implemented effectively to drive business impact. Storytelling & Communication: Present data insights clearly to both technical and non-technical stakeholders to inform product and business decisions.
Must-Have Requirements: Experience: 3 5 years in data collection/telemetry, product analytics, data science, or related fields with a proven track record of solving complex problems using data in real-world environments. SQL Expertise: Strong proficiency in SQL, especially in big data environments (e.g., working with distributed data warehouses like BigQuery, Snowflake, Redshift). AI/ML Skills: Hands-on experience applying machine learning algorithms to real-world problems such as product optimization, predictive modeling, user behavior analysis, or anomaly detection. Practical use of AI/ML to automate data collection workflows, detect and correct telemetry errors, and uncover hidden insights in user interaction data. Experience with feature engineering, model evaluation, and iteration in production settings. Understanding of AI Models: Solid understanding of AI models, including ML supervised and unsupervised techniques. Experience working on production-level AI applications, contributing to model deployment, monitoring, and continuous improvement pipelines. Programming Skills: Proficiency in Python or R for data analysis, statistical modelling, and implementing foundational to intermediate machine learning techniques using libraries such as Scikit-learn, TensorFlow, or PyTorch. Cross-Functional Collaboration: Proven ability to collaborate effectively with product managers, engineers, and designers to develop data-informed strategies and solutions. Communication Skills: Ability to distil and present complex data insights in a clear, actionable manner tailored to both technical and non-technical stakeholders.
Good-to-Have: Adobe Analytics: Experience with Adobe Analytics or Adobe Customer Journey Analytics. Clickstream Data Implementation: Familiarity with tools like Adobe Launch or Google Tag Manager (GTM) for product tracking strategies.
.
