You are a Data Science engineer who will work independently on understanding & scoping business problems, applying strong analytical thinking, and building production-ready solutions that move from insights to predictive modeling and actionable outcomes, with a strong foundation in data engineering and scalable data systems.

• *Key Responsibilities:**
• Work with product, business, and engineering teams to understand problem statements, clarify objectives, and translate them into structured analytical and data engineering tasks.
• Design, build, and maintain robust data pipelines to ingest, process, and transform large-scale structured and unstructured data for downstream analytics and ML use cases.
• Perform data analysis to identify trends, drivers, anomalies, and root causes, explaining both what is happening and why.
• Build, evaluate, and refine machine learning models (classification, regression, anomaly detection) with strong focus on feature engineering and validation.
• Develop and manage feature pipelines and data workflows that ensure consistency, reliability, and scalability of ML inputs.
• Design and develop ML-backed APIs, including load handling, pagination, and performance considerations.
• Collaborate closely with data engineering and platform teams to ensure data quality, governance, reliability, and scalability of end-to-end solutions.
• Deploy and maintain ML models in production, following best practices for reliability and lifecycle management (MLOps exposure is a plus).
• *Required Skills & Experience:**
• Robust hands-on experience in Python and applied Data Science / Machine Learning.
• Solid experience in Data Engineering, including building scalable data pipelines and working with large datasets.
• Proficiency in SQL for data extraction, transformation, analysis, and debugging.
• Experience with distributed data processing frameworks (e.g., Spark) and understanding of data architectures.
• Practical experience moving from EDA and insight generation to predictive modeling and operational use.
• Understanding of data structures & algorithms (DSA) relevant to efficient data processing and API-driven systems.
• Experience in developing with analytics/ML services APIs.
• *Good to Have:**
• Working knowledge of MLOps, including model deployment, monitoring, versioning, and CI/CD pipelines.
• Experience with cloud-based data platforms or modern data stack tools.
• Familiarity with data governance, data quality frameworks, and observability tools.
• *Role Expectations:**
• Own individual DS/ML and data engineering components end-to-end, including system-level design and data workflows.
• Clearly articulate insights, assumptions, and limitations to both technical and non-technical stakeholders.
• Focus on impact, correctness, scalability, and maintainability of solutions.
• *About Adobe:**

Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe's industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.

Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We're on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours. You are a Data Science engineer who will work independently on understanding & scoping business problems, applying strong analytical thinking, and building production-ready solutions that move from insights to predictive modeling and actionable outcomes, with a strong foundation in data engineering and scalable data systems.

• *Key Responsibilities:**
• Work with product, business, and engineering teams to understand problem statements, clarify objectives, and translate them into structured analytical and data engineering tasks.
• Design, build, and maintain robust data pipelines to ingest, process, and transform large-scale structured and unstructured data for downstream analytics and ML use cases.
• Perform data analysis to identify trends, drivers, anomalies, and root causes, explaining both what is happening and why.
• Build, evaluate, and refine machine learning models (classification, regression, anomaly detection) with strong focus on feature engineering and validation.
• Develop and manage feature pipelines and data workflows that ensure consistency, reliability, and scalability of ML inputs.
• Design and develop ML-backed APIs, including load handling, pagination, and performance considerations.
• Collaborate closely with data engineering and platform teams to ensure data quality, governance, reliability, and sc