Data Science Lead - Generative AI

As a data-science leader, you will be responsible for transforming raw data into actionable insights and developing innovative AI products. Your role will involve managing projects from the initial framing of business questions to the deployment and monitoring of models in the cloud. You will have the opportunity to introduce cutting-edge generative-AI concepts such as chat assistants and retrieval-augmented search. Additionally, you will lead a team of data professionals and collaborate closely with product, engineering, and business stakeholders to deliver measurable results. Your primary responsibilities will include designing and constructing predictive and forecasting models that drive tangible impact, as well as planning and executing A/B experiments to validate ideas and guide product decisions. You will be tasked with developing and maintaining data pipelines to ensure the integrity and timeliness of datasets. Furthermore, you will spearhead generative-AI initiatives, implement modern MLOps practices for model deployment and monitoring in the public cloud, and establish robust monitoring and alerting mechanisms for accuracy, latency, drift, and cost. In addition to your technical duties, you will play a crucial role in mentoring and coaching your team, conducting code reviews, and sharing best practices. You will be required to communicate complex findings in a clear and actionable manner to non-technical audiences. Upholding data governance and privacy standards across all projects will be paramount, in alignment with internal and industry guidelines. Furthermore, you will continuously assess new tools and methodologies, running proof of concepts to ensure that solutions remain cutting-edge. The ideal candidate for this role will possess a strong foundation in statistics, experiment design, and end-to-end machine learning workflows. Proficiency in Python and SQL is essential, along with a proven track record of transitioning models from notebooks to production environments. Hands-on experience with cloud platforms such as AWS, Azure, or GCP, including container-based deployment and CI/CD practices, is highly desirable. Practical exposure to generative-AI projects, including prompt engineering, fine-tuning, and retrieval-augmented pipelines, will be advantageous.,