Manager Data Science AI & Machine Learning - Hybrid
Job in
New York, New York County, New York, 10261, USA
Listed on 2026-07-03
Listing for:
Publicis Groupe
Full Time
position Listed on 2026-07-03
Job specializations:
-
IT/Tech
Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Scientist, Data Analyst
Job Description & How to Apply Below
Location: New York
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.
YourImpact
- 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
- 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.
- 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 decisioning and personalization in a hyperscale environment.
- Strong experience in Telecom or Retail use cases, including customer engagement, loyalty, retention, cross‑sell, upsell, and digital commerce optimization.
- Deep expertise in Recommendation Systems, Next Best Action (NBA), Next Best Offer (NBO), Personalization, Customer Segmentation, Churn Prediction, and Customer Analytics.
- Hands‑on expertise with Classification Models, Ensemble Methods, Anomaly Detection, Forecasting, and Statistical Modelling.
- 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 Tensor Flow, 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.
- Expertise in Model Evaluation and Experimentation, including deterministic vs. model‑based approaches, A/B testing, explainability, and business impact measurement.
- Ability to lead code reviews, mentor data scientists, and provide hands‑on technical leadership while remaining actively involved in solution delivery.
- Exceptional communication and consulting skills with the ability to translate complex AI and machine learning concepts into business value for senior…
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