Senior Associate Data Science/Machine Learning - Chicago - Hybrid
Sapient Corporation
Chicago, IL, United States
11 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Compensation
$117,000.0 - $160,000.0
Working hours
Regular working hours
Job source
Tech stack
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
+14 more
Tensorflow
Azure Machine Learning
Google Cloud
Cloud Platform System
Feature Engineering
Pytorch
Model Validation
Containerization
AI Platforms
Kubernetes
Data Analytics
Machine Learning Operations
Data Pipelines
Docker
Job description
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 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.
Requirements
- 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.
Benefits & conditions
- Access to ongoing learning and development opportunities.
- Competitive compensation and benefits package.
- Flexibility to support work-life balance.
- Comprehensive health benefits for you and your family.
- Generous paid leave and holidays.
- Wellness program and employee assistance.
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