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# Senior Associate Data Science/Machine Learning - Chicago - Hybrid

- **Company:** Publicis Groupe
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $117,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Big Data, Cloud Computing, Continuous Integration, Data Cleansing, Information Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Scripting, Google Cloud, Cloud Platform System, Feature Engineering, Pytorch, Model Validation, Containerization, AI Platforms, Kubernetes, Data Analytics, Machine Learning Operations, Data Pipelines, Docker
- **Published:** October 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-associate-data-science-machine-learning-chicago-hybrid-chicago-il--713b545b-d0e8-4c14-80dc-60653044e6b7

## About the Role

Machine Learning, Data Science, Consulting, Startup, Data Science, Customer Experience, Best Practices, Cloud Computing, Cross-Functional, Python Programming/Scripting Language, optimize data-driven solutions that drive business impact. Your expertise in ML engineering will be crucial in building robust, scalable, and efficient AI systems. Your Impact Design, develop, and deploy machine learning models to solve complex business problems. Collaborate with data engineers, product managers, and business stakeholders to implement end-to-end ML solutions. Optimize and scale machine learning pipelines for performance and efficiency. Ensure the reliability, scalability, and maintainability of AI/ML systems in production. Utilize cloud-based platforms (AWS, GCP, Azure) for model training, deployment, and monitoring. Implement best practices in data preprocessing, feature engineering, and model evaluation. Stay updated with the latest advancements in ML engineering and AI technologies. Qualifications Skills & Experience 5+ years of experience in machine learning, data science, or AI engineering. Strong expertise in ML model development, deployment, and optimization.
Proficiency in Python, TensorFlow, PyTorch, or other ML frameworks. Experience with MLOps, CI/CD pipelines, and model monitoring. Hands-on experience with cloud-based ML platforms (AWS SageMaker, GCP AI Platform, Azure ML). Strong understanding of data engineering, data pipelines, and big data technologies. Excellent problem-solving and analytical skills. Set Yourself Apart With Experience in building and deploying large-scale AI/ML systems. Knowledge of Kubernetes, Docker, and other containerization technologies. Expertise in time-series forecasting, NLP, or computer vision. Certifications in ML engineering, MLOps, or cloud platforms. Additional information Additional information: Salary Range: $117,000 - $160,000 The ranges shown above represent a grouping of relevant ranges currently in use at Publicis Sapient. The actual range for this position may vary based on location and the specific skills required for the role. Benefits include: Access to ongoing learning and development

## Description

Company description Publicis Sapient is a digital transformation partner helping established organizations get to their future, digitally enabled state, both in the way they work and the way they serve their customers. We help unlock value through a start-up mindset and modern methods, fusing strategy, consulting, and customer experience with agile engineering and problem-solving creativity. United by our core values and our purpose of helping people thrive in the brave pursuit of next, our 20,000+ people in 53 offices around the world combine experience across technology, data sciences, consulting, and customer obsession to accelerate our clients' businesses through designing the products and services their customers truly value. Overview Senior Associate Data Science - Chicago Hybrid As a Senior Associate Data Scientist, you will be responsible for developing and deploying machine learning models at scale. You will work closely with cross-functional teams to design, implement, and