Data Scientist 3 - Machine learning / Python

Job Description

As a Data Science engineer at Adobe, you will independently work on understanding and scoping business problems, apply strong analytical thinking, and build production-ready solutions that move from insights to predictive modeling and actionable outcomes.Key Responsibilities:- Work with product, business, and engineering teams to understand problem statements, clarify objectives, and translate them into structured analytical tasks.- 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 a strong focus on feature engineering and validation.- Deploy and maintain ML models in production, following ML Ops best practices such as versioning, CI/CD integration, monitoring, and retraining.- Design and develop ML-backed APIs, including load handling, pagination, and performance considerations.- Collaborate with data engineering and platform teams to ensure data quality, reliability, and scalability of ML solutions.Required Skills & Experience:- Strong hands-on experience in Python and applied Data Science/Machine Learning.- Practical experience moving from EDA and insight generation to predictive modeling and operational use.- Working knowledge of ML Ops, including model deployment, monitoring, and CI/CD pipelines.- 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:- Exposure to Data Engineering concepts such as Spark fundamentals and distributed computing.- Strong working knowledge of SQL for analysis, debugging, and data validation.Role Expectations:- Own individual DS/ML components end-to-end and associated system-level design.- Clearly articulate insights, assumptions, and limitations to both technical and non-technical stakeholders.- Focus on impact, correctness, and maintainability.About Adobe:Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity, and personalized customer experiences. Adobes industry-leading offerings enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Join us in driving the next decade of growth and innovation.Adobe is proud to be an Equal Employment Opportunity employer, fostering diversity and inclusion in the workplace. If you require accommodation during the application process, please contact hidden_email.AI Use Guidelines for Interviews:Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately, your application may not move forward in the process.At Adobe, we empower employees to innovate with AI and look for candidates eager to do the same. Join us in advancing our mission of empowering everyone to create. As a Data Science engineer at Adobe, you will independently work on understanding and scoping business problems, apply strong analytical thinking, and build production-ready solutions that move from insights to predictive modeling and actionable outcomes.Key Responsibilities:- Work with product, business, and engineering teams to understand problem statements, clarify objectives, and translate them into structured analytical tasks.- 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 a strong focus on feature engineering and validation.- Deploy and maintain ML models in production, following ML Ops best practices such as versioning, CI/CD integration, monitoring, and retraining.- Design and develop ML-backed APIs, including load handling, pagination, and performance considerations.- Collaborate with data engineering and platform teams to ensure data quality, reliability, and scalability of ML solutions.Required Skills & Experience:- Strong hands-on experience in Python and applied Data Science/Machine Learning.- Practical experience moving from EDA and insight generation to predictive modeling and operational use.- Working knowledge of ML Ops, including model deployment, monitoring, and CI/CD pipelines.- 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:- Exposure to Data Engineering concepts such as Spark fundamentals and distributed computing.- Strong working knowledge of SQL

Keyskills :
PythonData ScienceMachine LearningEDASQLSparkML Ops