Manager - Data Science & AI

What makes this a great opportunity?

As the Manager of Data Science & AI, you will play a key leadership role in delivering AI and advanced analytics solutions that translate complex business challenges into measurable business impact. Reporting to the Director of Data Science & AI, you will lead multiple Data Science workstreams end-to-end, from problem definition and scoping through development, deployment, experimentation, and measurement. This role requires a highly technical, hands-on leader who can manage multiple resources and projects while effectively partnering with business stakeholders in a highly ambiguous environment.

Qualifications

General Qualifications

Education: Master's degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Physics, or a closely related quantitative field. Advanced degree strongly preferred.

Experience: Minimum of 5 years in Data Science, Machine Learning, or Advanced Analytics with progressive responsibility and demonstrated leadership impact. Four or more years building and deploying production AI/ML solutions in enterprise environments. Proven success leading data science programs in a global CPG, beverage, or similar fast-moving consumer-goods environment preferred.

Technical Proficiency: Deep expertise in machine learning, AI, and statistical modeling with proficiency in Python, PySpark, SQL, and advanced analytics platforms. Working proficiency in cloud-native AI architectures (Google Cloud Platform, DataBricks), and scalable AI deployment. Experience with generative AI, deep learning frameworks, and modern AI development practices is nice to have. Strong background in A/B testing, attribution modeling, demand forecasting, and recommendation engines preferred.

Leadership and Communication: Proven leadership skills with the ability to manage cross-functional teams and communicate complex data concepts to non-technical stakeholders effectively. Demonstrated project management skills, including the ability to scope, prioritize, and deliver complex data science and AI initiatives on time and within resource constraints. Outstanding PowerPoint and presentation skills required.

Strategic and Commercial Acumen: Strong strategic thinking and business acumen with the ability to translate AI/ML capabilities into measurable business value. Track record of delivering advanced analytics solutions that drive revenue growth, cost optimization, and competitive advantage.

Innovation Mindset: A strong track record of leveraging emerging AI technologies and data science methodologies to drive transformational business outcomes and establish competitive differentiation.

Preferred Qualifications

Industry Knowledge: Deep understanding of the beverage and alcohol manufacturing sector, including global market dynamics, consumer behaviors, regulatory constraints, and distribution complexities.

Commercial Analytics: Extensive experience in revenue growth management (RGM), brand investment optimization, and pricing analytics for CPG companies. Background in leveraging AI to drive commercial decisions, optimize promotional effectiveness, and enhance portfolio management in the beverage and alcohol sector.

Regulatory Knowledge: Understanding of data protection laws, AI ethics, compliance regulations, and responsible AI practices within the global beverage and alcohol industry.

Advanced AI Specializations: Experience with multimodal AI systems, computer vision for product and marketing applications, LLMs for consumer insights, and advanced forecasting and optimization models.

Role Responsibilities

Leadership and Strategy: Help define and execute the Data Science & AI strategy in alignment with corporate objectives. Lead and manage multiple Data Science resources across concurrent projects, providing direction, coaching, prioritization, and accountability for delivery.

Technical Excellence: Serve as a hands-on technical leader with strong programming capabilities. Drive the development of scalable Data Science solutions using Databricks, PySpark, Python, SQL, and Google Cloud Platform (GCP). Oversee data pipelines, machine learning models, ML Ops frameworks, and production deployments while establishing strong engineering practices including Git/GitHub, code reviews, unit testing, documentation, reproducibility, and CI/CD.

Product Recommendation & Advanced Analytics: Lead the development, deployment, and continuous improvement of product recommendation engines, next-best-product, next-best-action, ranking, and other decision-support solutions. Apply machine learning, statistical modeling, business rules, and commercial data to generate actionable recommendations and measurable business value.

Experimentation and Measurement: Design and lead A/B testing, test-and-control methodologies, experimental design, statistical analysis, and business impact measurement. Establish rigorous frameworks to quantify incremental sales impact, ROI, and effectiveness of Data Science solutions.

Stakeholder Engagement: Serve as a trusted advisor to business and technology stakeholders. Work effectively with senior leaders and cross-functional teams to translate complex business problems into actionable Data Science opportunities. Strongly manage stakeholder expectations, communicate clearly, facilitate decisions, and maintain alignment throughout the project lifecycle.

Problem Definition and Delivery: Operate effectively in highly ambiguous environments with messy, incomplete, or fragmented data. Bring structure to loosely defined problems, assess data feasibility, define realistic scope and success criteria, and independently lead projects and workstreams from end-to-end with minimal managerial oversight.

Performance and Governance: Establish and monitor KPIs for adoption, data quality, system performance, and business impact. Drive continuous improvement while ensuring solutions are scalable, reliable, reproducible, and appropriately governed.

Organizational Capability Building: Promote Data Science best practices and build team capabilities through coaching, knowledge sharing, technical standards, and development processes. Help scale the Data Science function to support future organizational growth.

Commercial Impact and Industry Expertise: Lead advanced analytics initiatives that drive sales productivity, revenue growth management (RGM), product recommendation effectiveness, consumer insights, forecasting, and operational efficiency. Apply strong business judgment to identify where Data Science can create meaningful commercial value.