Integrated Business Planning (IBP) brings together forecasting and planning across marketing, sales, finance, procurement, manufacturing, and customer service into a unified process. This integration ensures alignment across the enterprise, leading to superior customer outcomes.
As a Demand Planning Data Scientist, you will build statistical models and deliver insights to guide business decisions related to demand forecasting that provide reliable and accurate signals for inventory and supply plans. You will partner closely with an IBP demand planner(s) to generate reliable forecasts so they can coordinate demand reviews. These roles in collaboration together will be crucial in ensuring effective demand schedules, streamlining internal processes, and ultimately enhancing the customer experience.
Principle Duties/Responsibilities:
Develop Statistical Forecasts: Lead the development and implementation of predictive models to forecast product orders across various geographies. Utilize historical data, machine learning, market intelligence, and field sales input to create highly accurate demand forecasting models.
Assist Demand Planner(s) with Root Cause Analysis: Investigate key metrics such as forecast accuracy, bias, and value add to identify underlying issues.
Evaluate IBP Deviations: Collaborate with Demand Planners to discuss and understand the impact of last month's Integrated Business Planning (IBP) deviations on the current month's plan.
Generate Analytical Insights: Theorize potential insights, design and execute experiments to validate model performance and improve forecasting accuracy.
Communicate Analytical Gains: Articulate the benefits of productionizing statistical models to business functions that can leverage the results of analytical processes.
Create and Validate Statistical Models: Develop statistical models to test hypotheses and ensure they can be productionized for regular use, allowing for consistent sharing of analysis results. Analyze large datasets to identify trends, patterns, and insights that inform business decisions.
Model Complex Organizational Problems: Use statistical, algorithmic, mining, and visualization techniques to uncover insights and identify opportunities within complex organizational challenges.
Educate on New Approaches: Inform and educate the organization on innovative analytical approaches and methodologies.
Identify Data Science Opportunities: Recognize problems that would benefit from Data Scientist expertise. Communicate findings and recommendations to stakeholders through clear and compelling visualizations and reports.
Strategic Data Recommendations: Provide strategic recommendations on data collection, integration, and retention requirements, incorporating business needs and best practices. Collaborate with cross-functional teams to integrate data-driven strategies into business planning processes.
Drive Innovation and Improvement: Stay updated with the latest advancements in data science and machine learning to continuously enhance our forecasting capabilities.
Qualifications
Bachelor's or Master's Degree in Data Science, Statistics, Computer Science, or a related field.
Typically, at least 1-2+ years of experience in data science or equivalent work experience.
Extensive knowledge in predictive modeling, machine learning, and statistical analysis.
Proficiency in programming languages such as Python, R, or SQL.