Manager Data Science AI & Machine Learning - Hybrid 4 days

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

Manager Data Science - Hybrid (4 days) NYC

The Manager, Data Science is to lead and work with clients undergoing a data-driven transformation (DDT) across the globe. The Manager will accelerate and drive the DDT strategy for clients, working in partnership with digital directors, client teams, and practice capability. Furthermore, the role will be a key contributor in ensuring the agency is an industry leader in digital thinking, execution, and value realization for clients through data-driven solutions.

Your Impact:

• You’re passionate about solving real world problems through the application of machine learning and AI
• Consult with clients to define business problems, and advise on how to apply data science to solve those problems
• Effectively communicate complex technical concepts, processes and results to non-technical audiences
• Solve some of today’s most complex customer issues by designing, coding and implementing machine learning and AI solutions for our clients, or in support of our AI platforms. Engage across capabilities to ensure project delivered to client expectations
• Collaborate and knowledge-share with your colleagues by sharing the latest research new data science methods, technologies, and thinking to optimize client solutions and outcomes, as well as through detailed, constructive design and code reviews. Provide expertise and point of view on statistical & machine learning methods and applications to colleagues and clients. Commit time to training junior team members
• Help establish rigorous standards in machine learning and statistical analysis to ensure consistency across projects. Contribute to the advancement of data science as a capability
• Provide internal expertise and point of view on data science applications. Ability to apply techniques and learnings from past projects to new projects
• Contribute to identification of new opportunities within the data science space and support the delivery of projects on time and to the highest possible standards.
• Effectively manage a small team through constructive, honest feedback, daily leadership and motivation being accountable for project quality and delivery

Qualifications

Your Skills & Experience

• Master’s or PhD in Data Science, Computer Science, AI, Statistics, Mathematics, Engineering, or related quantitative field.
• 9+ years of hands-on experience designing, building, and deploying machine learning solutions in production environments.
• Deep expertise in Recommendation Systems, Next Best Action (NBA), Next Best Offer (NBO), Personalization, Customer Segmentation, Churn Prediction, and Customer Analytics.
• Strong experience in Telecom or Retail use cases, including customer engagement, loyalty, retention, cross-sell, upsell, and digital commerce optimization.
• Hands-on expertise with Classification Models, Ensemble Methods, Anomaly Detection, Forecasting, and Statistical Modeling.
• Experience developing NLP and Generative AI solutions, including Sentiment Analysis, Text Classification, Transformer Models, LLMs, and RAG applications.
• Strong programming skills in Python and SQL, with experience using TensorFlow, PyTorch, Scikit-Learn, XGBoost, or similar ML frameworks.
• Hands-on experience with GCP and Vertex AI, including model development, deployment, monitoring, and optimization.
• Experience implementing Feature Stores, Feature Engineering Pipelines, and Reusable ML Assets for enterprise-scale AI solutions.
• Strong understanding of MLOps, including CI/CD, Model Versioning, Experiment Tracking, Model Monitoring, Drift Detection, and Automated Retraining.
• Experience deploying AI/ML solutions into large-scale production environments supporting real-time deci…