Manager - Data Science & AI

What makes this a great opportunity?

As a Data Science manager, you will lead analytics workstreams inside our centralized Data Science & AI team, setting the gold standard for data science practices across the organization. This critical role is not just about delivering advanced analytics solutions; it is about being at the heart of transforming data into actionable insights that drive strategic decisions across business functions. You will have the unique opportunity to apply decision sciences to influence the company's direction, optimize operations, and enhance our competitive edge in the market. This role is perfect for a leader passionate about leveraging data science to unlock new opportunities and efficiencies in a dynamic industry setting. 

This role offers the chance to be at the forefront of data science innovation in the beverage and alcohol industry, leading a team that directly contributes to our strategic goals and operational excellence. If you are a leader driven by the power of data to transform business, we invite you to apply. 

Role Responsibilities

Advanced Analytics Solutions: Drive the development and delivery of advanced analytics projects, applying predictive modeling, machine learning, generative AI, and other data science methods to deliver actionable insights for various business functions and markets. 

Standards and Best Practices Development: Establish and promote a set of standards and best practices for data science, ensuring consistency, quality, and impact of data science projects across the company. 

Strategic Collaboration: Partner with business leaders, IT, and other stakeholders to identify opportunities for data science initiatives that support business objectives and drive value. 

Innovation and Research: Stay abreast of emerging data science technologies and methodologies, exploring new tools and techniques that can enhance the organization's analytical capabilities. 

Impact Measurement: Develop and monitor key performance indicators (KPIs) to evaluate the effectiveness of data science initiatives and their impact on business outcomes. 

Qualifications

Education: Bachelor's or Master's degree in Analytics, Data Science, Computer Science, Statistics, Mathematics, Physics or a related quantitative field. 

Experience: At least 5+ years of full-time experience in data science, with a proven track record of managing data science teams and delivering impactful analytics projects, preferably in the beverage and alcohol or other CPG industries. 

Technical Expertise: Deep knowledge of machine learning, predictive analytics, and statistical modeling techniques, with proficiency in data science tools and programming languages such as Python, R, SAS, or Scala. 

Strategic Thinking: Strong strategic and analytical thinking skills, with the ability to translate complex data insights into actionable business strategies. 

Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex data science concepts to non-technical stakeholders. 

Industry Knowledge (Nice to Have): Understanding of the beverage and alcohol manufacturing sector, including market dynamics, customer behaviors, and regulatory constraints.   

Commercial Analytics (Nice to Have): Experience in RGM (revenue growth management), evaluating brand investment effectiveness, and demand forecasting for a CPG company. A background in leveraging analytics to drive commercial decisions, optimize pricing strategies, promotional effectiveness, and portfolio management in the beverage and alcohol sector is highly desirable.

Technical Qualifications:

Mathematics: Understanding of linear algebra, probability theory, calculus, and discrete math. 

Statistics: Proficiency in descriptive statistics, inferential statistics (including Bayesian inference), hypothesis testing, design of experiments, statistical modeling, A/B testing, and data sampling techniques. 

Database Management: Proficiency in querying and managing databases to extract insights and optimize data operations.   

Data Science: Experience with building descriptive, diagnostic, predictive, and prescriptive models using supervised and unsupervised learning techniques.  Techniques include clustering, regressions, decision trees, random forests, gradient boosting, support vector machines (SVM), natural language processing (NLP), ensemble modeling, etc.        

Machine Learning & Deep Learning: Experience with applying deep learning, computer vision, and generative AI models (i.e. CNNs, RNNs, GANs, LLMs, etc.) on commercial use cases.  Familiarity with common machine learning frameworks and tools such as TensorFlow, PyTorch, Keras, or related deep learning frameworks.  Exposure to multimodal generative AI systems.