Assistant Director, Data Science, Modeling Sophistication

Design, train, and deploy computer vision and deep learning models, from research and experimentation through production implementation. Collaborate with business stakeholders to deliver data products such as feature pipelines, predictive models, dashboards, and datasets derived from image data. Develop and maintain scalable data pipelines and model workflows, applying MLOps best practices for reproducibility, deployment, and monitoring. Research and prototype new methodologies for training, evaluating, and improving deep learning models, particularly for computer vision. Integrate model outputs into business applications and partner with engineering teams to operationalize models in production environments. Contribute to the design, construction, and validation of large and complex datasets in collaboration with cross-functional science teams. Communicate findings through technical presentations, reports, and recommendations to both technical and non-technical stakeholders. Participate in cross-functional working groups and contribute to the broader data science community to promote best practices. Demonstrated expertise in deep learning with an emphasis on computer vision. Strong foundation in machine learning, statistics, experimental design, and model evaluation metrics. Proficiency in Python and MLOps practices, with experience in version control (Git), code review, collaborative development workflows (e.g., GitHub/GitLab), and model versioning/experiment tracking (e.g., MLflow). Proficiency in deep learning frameworks such as PyTorch (preferred) or TensorFlow, with experience in model design, training, and deployment. Experience building and managing pipelines with workflow orchestration tools (e.g., Airflow, Luigi). Experience with Docker and CI/CD pipelines. Experience with container orchestration systems such as Kubernetes. Understanding of GPU acceleration, distributed training, and model optimization techniques (e.g., mixed precision, pruning, quantization). Experience with multimodal learning, including vision-language models and cross-modal representation learning. Track record of advancing research projects from ideation to implementation. Broad knowledge of predictive analytic techniques and statistical diagnostics of models. Expert knowledge of predictive toolset; reflects as expert resource for tool development. Demonstrated ability to exchange ideas and convey complex information clearly and concisely. Networks with key contacts outside own area of expertise. Ability to establish and build relationships within the aligned functional area or SBU. Ability to give effective training and presentations to peers, management and less senior business leaders. Ability to use results of analysis to persuade team or department management to a particular course of action. Has a value driven perspective with regard to understanding of work context and impact. Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 2 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelor`s degree(scientific field of study) and a minimum of 5 years of relevant experience.