Manager, Data Science and Operations Research

The Manager of Data Science and Operations Research (DSOR) is a crucial leadership position responsible for enabling value creation through the application of Data Science and Operations Research methodologies. This support role requires a blend of strategic business thinking, product design, project management, user empathy, ability to structure the work with stakeholders, and proactive communication of progress or challenges. The Manager will closely partner and support a team of DSOR practitioners, fostering a culture of curiosity, rapid experimentation, and continuous improvement. This position is pivotal in translating business ideas into actionable solutions, ensuring progress through strategic enablement support, design excellence, and championing the use of the scientific method across the enterprise., * Builds and promotes advanced analytics and data science capabilities, to extract insights from vast data resources and inform decision-making across the business.

• Hiring, coaching, performance management, and strategic planning, as well as staying current with the latest developments in data science and collaborating with IT and business leadership on advanced analytics initiatives.
• Advanced Quantitative Background: Master’s or Ph.D. in Computer Science, Operations Research, Data Science, Machine Learning, Artificial Intelligence, Statistics, or another quantitative and computational field.
• Strategic AI & ML Leadership: Proven track record of leading and scaling high-performing data science teams to design and deliver advanced machine learning, predictive analytics, and generative AI solutions that drive measurable business value.
• ML Strategy & Experimental Oversight: Direct experience defining the strategic machine learning approach for complex business problems. Proven ability to guide teams through robust feature engineering, experimental design, and model development, while establishing metrics to rigorously measure and ensure model performance, accuracy, and business impact.
• Scientific Method & Experimental Rigor: Strong mastery of the scientific method; proficiency and comfort guiding teams through rigorous hypothesis definition, rapid prototyping, and structured experimentation.
• Opportunity Mobilization: Proven ability to mobilize cross-functional teams to explore new opportunities, foster innovation, and drive continuous experimentation.
• User-Centric Product Vision: Ability to envision and actively contribute to the design of user-centric products that seamlessly integrate data science to drive tangible business value and user engagement.
• Pragmatic & Business-Aligned Decision Making: Strong business acumen with an “engineering” mentality-capable of making pragmatic, rapid trade-offs between model complexity, development velocity, and business impact.
• Modern Software Best Practices: Experience championing high standards for code quality, collaborative workflows (Git-based version control), and reusable, production-ready data science repositories.
• Deep Analytical Empathy:
• Solid theoretical grounding in the mathematical and statistical core of data science to effectively evaluate team methodologies.
• Ability to critically review and guide the application of advanced modeling, deep learning, or neural network architectures.
• MLOps & Lifecycle Advocacy: Strong conceptual understanding of DevOps, MLOps, and LLMOps practices(CI/CD, model monitoring, and automated pipelines) to support the seamless transition of your team’s models into production environments.
• Oversee the organization and strategic management of a portfolio of diverse DSOR projects. Ensure clear milestones are defined, progress is tracked systematically, and high-quality deliverables are met on time.
• Govern incoming business demand, set clear priorities, manage team capacity, and establish strict accountability for project progress., Bachelor’s degree or equivalent in Data Science, Operations Research, Engineering, Business Administration, Business Analytics, or related discipline. [Advanced degree preferred.], Five to eight (5 - 8) years of experience in working with teams in analytics, data science, operations research, engineering or a related field, with demonstrated leadership in managing teams and executing August 21, 2024 Version: 2 Page 3 of 3 complex projects. Practitioner or practitioner-adjacent experience in analytics, data science, operations research, or a related fields inclusive of prototyping and rapid experimentation.