Machine Learning Developer building robust data pipelines and scale ML systems at Morgan Stanley. Collaborating with Data Scientists and Engineers to transform big data into actionable insights.
Responsibilities
Design, develop, and maintain data pipelines for ingesting, transforming, and analyzing large-scale trading datasets.
Implement and productionize advanced analytics and machine learning workflows—leveraging Spark, Python and modern big data technologies.
Collaborate closely with Data Scientists and Engineering team members to operationalize models and automation.
Optimize code and data workflows for performance, scalability, and reliability.
Leverage MLOps components, automate feature engineering as part of robust, production ML workflows.
Write clean, well-tested, well-documented code and participate in code reviews.
Requirements
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field.
5+ years of relevant professional experience in software development, data engineering, or machine learning applications.
Programming: Proficiency with Python (ex. Pandas, NumPy, Jupyter Notebooks), SQL for data engineering and analytics.
Exposure and/or interest in the latest technologies in machine learning, AI (Modern AI: Generative AI, Multi-Agent Systems, MCP, Agentic AI architectures) and big data; advocate for best practices across the team.
Highly Desirable Skills (Any of the following): Experience with Java, Scala (e.g. Spark)
Big Data & ML Platforms: Spark, Databricks, Snowflake, Azure
Machine Learning & MLOps: Feature stores, model registry, MLOps frameworks, continuous model monitoring and retraining, scalable model deployment
Front-end Development: React (for building dashboards or UI integrations)
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