Staff Data Scientist

• Join the Product AI team within Adobe Experience Cloud, a high-impact group that applies machine learning and advanced statistical modeling to improve how customers adopt and realize value from client’s enterprise products.
• We are seeking a Senior Data Science Engineer – Applied ML to deploy, improve and maintain predictive models that identify customer risk, detect anomalies in product usage, and generate actionable insights for product and customer success teams.
• A key focus of this role will be building Early Warning Systems (EWS) that identify customers at risk of declining engagement or churn.
• In this role, you will collaborate closely with product managers, engineers, and other data scientists to transform behavioral data into actionable intelligence that drives product improvements and proactive customer engagement.

• Maintain and improve scalable ML pipelines using Databricks, Spark, and Python to support model training, scoring, and monitoring.
• Operationalize existing ML models by integrating them into automated pipelines for batch scoring and reporting.
• Develop workflows for model retraining, evaluation, and monitoring to ensure models remain reliable as data evolves.
• Ensure models and pipelines are integrated with production data systems.

Anomaly Detection & Data Monitoring

• Develop and maintain anomaly detection systems across datasets to identify unusual changes in product usage patterns or data quality issues.
• Apply statistical and machine learning approaches to detect anomalies in product telemetry and customer engagement signals.
• Build monitoring frameworks that help identify shifts in data distributions, behavioral trends, or model performance.

Early Warning Systems & Predictive Modeling

• Implement and scale Early Warning System (EWS) models that identify customers at risk of declining engagement or churn across multiple Adobe products.
• Replicate and adapt existing predictive models to new products and datasets, ensuring models generalize across different product usage patterns.
• Evaluate model performance across products and refine features or modeling approaches as needed.

Communication & Collaboration

• Partner with engineers, and other data scientists to improve customer engagement and adoption strategies.
• Present findings through visualizations, reports, and technical documentation.
• Promote best practices in reproducible data science, model validation, and scalable ML workflows.

• 8+ years of experience in data engineering/science, machine learning, or related quantitative roles.
• Strong programming skills in Python and SQL, with experience using libraries such as Pandas, NumPy, and Scikit-learn.
• Experience implementing and operationalizing machine learning models in production environments.
• Experience working with large-scale data platforms such as Databricks, Spark, or similar distributed data frameworks.
• Experience operationalizing and maintaining predictive models in B2B settings.
• Experience implementing anomaly detection techniques for large datasets.
• Experience applying model explainability methods (e.g., SHAP, LIME, feature attribution).
• Strong understanding of ML model evaluation techniques.
• Experience developing feature pipelines and ML workflows that support scalable model training and scoring.
• Strong communication skills with the ability to translate technical findings into business insights.

Preferred Qualifications

• Experience working with product usage analytics or digital product telemetry data.
• Experience developing anomaly detection systems for large-scale behavioral datasets.
• Experience working in B2B SaaS or enterprise product analytics environments.
• Exposure to LLMs or Generative AI, including embeddings or retrieval-based systems