Unclassified - Arlington, VA
posted about 2 months ago
The Senior Data Science Engineer at Publicis Sapient plays a pivotal role in driving data-driven transformations (DDT) for clients globally. This position is designed for individuals who are passionate about leveraging machine learning and artificial intelligence, particularly generative AI, to solve real-world problems. The Senior Associate will collaborate closely with digital directors, client teams, and practice capabilities to accelerate and implement DDT strategies effectively. This role is crucial in ensuring that Publicis Sapient remains an industry leader in digital thinking, execution, and value realization through innovative data-driven solutions. In this position, the Senior Data Science Engineer will be responsible for developing and implementing generative AI models and algorithms, including Retrieval Augment generation and prompt engineering. Candidates should possess the ability to work with large datasets and have a strong understanding of preprocessing techniques. Experience in building and deploying multi-modal generative AI systems in real-world applications is essential, along with familiarity with the GCP ecosystem, particularly ImageGen, Vector Search, and Vertex AI. The role also involves leading the development and implementation of advanced machine learning models, ensuring high standards of technical excellence. The Senior Data Science Engineer will design and implement ML engineering workflows, streamlining the deployment of models and systems to production for optimal efficiency. A strong understanding of machine learning and deep learning principles is required, along with exceptional ML engineering knowledge to manage complex ML workflows effectively. Candidates should have extensive full-time experience in Data Science roles, with a proven track record of leading and mentoring small data science project teams. The ability to successfully take multiple data science models and systems into production using the GCP ecosystem is crucial. Additionally, expertise in Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps) is necessary, along with proficiency in collaborative development tools and practices, including peer code reviews and version control (Git).