Description
Publicis Sapient is looking for an experienced Manager, Machine Learning Engineering to lead our talented team of AI and data science experts. In this influential role, you will be responsible for developing and implementing solutions that address complex business challenges across a wide range of industries. Your expertise will empower clients to revolutionize their businesses by harnessing the potential of advanced technology. As a Manager, Machine Learning Engineering, you will collaborate with cross-functional teams to strategize, develop, and deliver machine learning models tailored to meet specific business objectives. You will be responsible for overseeing the entire lifecycle of these models, from data preprocessing and algorithm selection to performance evaluation and seamless integration into production systems.
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Your Impact:
- Provide strategic direction and overseeing the execution of machine learning initiatives to address specific business needs.
- Lead and manage a team of Machine Learning Engineers in designing, developing, and implementing machine learning models.
- Collaborate with cross-functional teams to understand business requirements and strategize innovative solutions.
- Oversee the collection, preprocessing, and analysis of large datasets to identify patterns, trends, and relationships for training machine learning models.
- Guide the selection and implementation of machine learning algorithms and models, including feature engineering, optimization, and evaluation.
- Ensure the development and maintenance of robust, scalable, and reusable code, with proper version control.
- Establish and enforce MLOps best practices to streamline the development, deployment, and monitoring of machine learning models in production environments
Qualifications
- Gender Neutral Policy
- 18 paid holidays throughout the year
- Generous parental leave and new parent transition program
- Flexible work arrangements
- Employee Assistance Programs to help you in wellness and well being
Additional Information
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 the 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.
Company Description
Publicis Sapient is looking for an experienced Manager, Machine Learning Engineering to lead our talented team of AI and data science experts. In this influential role, you will be responsible for developing and implementing solutions that address complex business challenges across a wide range of industries. Your expertise will empower clients to revolutionize their businesses by harnessing the potential of advanced technology. As a Manager, Machine Learning Engineering, you will collaborate with cross-functional teams to strategize, develop, and deliver machine learning models tailored to meet specific business objectives. You will be responsible for overseeing the entire lifecycle of these models, from data preprocessing and algorithm selection to performance evaluation and seamless integration into production systems.
Your Impact:
- Provide strategic direction and overseeing the execution of machine learning initiatives to address specific business needs.
- Lead and manage a team of Machine Learning Engineers in designing, developing, and implementing machine learning models.
- Collaborate with cross-functional teams to understand business requirements and strategize innovative solutions.
- Oversee the collection, preprocessing, and analysis of large datasets to identify patterns, trends, and relationships for training machine learning models.
- Guide the selection and implementation of machine learning algorithms and models, including feature engineering, optimization, and evaluation.
- Ensure the development and maintenance of robust, scalable, and reusable code, with proper version control.
- Establish and enforce MLOps best practices to streamline the development, deployment, and monitoring of machine learning models in production environments