Manager, Data Science

Manager, Data Science

The Manager, Data Science leads a specialized team of data professionals varying in size and complexity that are responsible for aiding to drive changes and improvement in business practices through data science. This role manages the overall data scientist team or function for a key business which may include Modelers and/or Data Scientist roles. This role may also oversee the development of data models, data mining and analytic solutions.

Day to day:

• Lead and mentor a team of Data Scientists, providing technical guidance, coaching, performance feedback, and career development support.
• Partner with business stakeholders to identify, prioritize, and deliver machine learning, AI, and advanced analytics use cases.
• Translate business requirements into analytical frameworks, machine learning models, and actionable recommendations.
• Develop, test, and deploy predictive, prescriptive, and generative AI solutions to address business challenges.
• Oversee model development activities including feature engineering, algorithm selection, model training, validation, and performance monitoring.
• Work closely with data engineers and technology teams to ensure scalable, production-ready solutions.
• Conduct exploratory data analysis and communicate findings to both technical and non-technical audiences.
• Support proof-of-concepts, pilot programs, and experimentation initiatives to evaluate new AI and machine learning capabilities.
• Ensure model governance, documentation, validation, and monitoring requirements are met in accordance with enterprise standards.
• Evaluate emerging data science techniques, AI technologies, and industry trends and recommend practical applications.
• Present insights, findings, and recommendations to management and business partners.
• Contribute to establishing best practices, reusable frameworks, and standards across the data science function.

Depth & Scope:

• Provides people management leadership by hiring the best talent, setting goals, developing staff, managing employee performance and compensation decisions, promoting teamwork and handling any/all disciplinary actions, as required
• Leads and manages a sizeable team of Data Science professionals and overall operation of a diverse group in an area of moderate risk, complexity or scope
• Ensures an integrated approach with other business management areas, broader organization, and enterprise as appropriate
• Deep knowledge and understanding of businesses/technology, and organizational practices/disciplines
• Sound to advanced knowledge of external competition, industry and/or market trends in relation to own function/business
• Focuses on short to medium-term issues (e.g. 6-12 months)

Education & Experience:

• Undergraduate degree or advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science) Graduate's degree preferred with either progressive project work experience, or;
• 5+ year of relevant experience; higher degree education and research tenure can be counted

Preferred Qualifications:

• "Big Tech" experience strongly preferred
• Master's degree in data science, PhD in data science, Computer Science, Statistics, Mathematics, Machine Learning, Artificial Intelligence, Engineering, Physics, or a related quantitative field.
• 5+ years of experience applying machine learning, artificial intelligence, advanced analytics, or statistical modeling techniques in a business environment.
• 2+ years of experience leading projects, mentoring junior team members, or providing technical leadership.
• Advanced proficiency in Python and modern data science libraries and frameworks (e.g., Pandas, Num Py, Scikit-learn, Tensor Flow, PyTorch, XGBoost).
• Demonstrated experience developing and implementing predictive models and advanced analytics solutions that drive business outcomes
• Experience building and deploying machine learning models in production environments.
• Experience developing Generative AI, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), Agentic AI, or Natural Language Processing (NLP) solutions.
• Strong understanding of machine learning techniques including classification, regression, clustering, forecasting, anomaly…