Demand Planning Analyst

About the Role

Join Entegris as a Demand Planning Analyst, a key contributor within the Demand Planning team focused on creating, deploying, and managing analytical solutions aimed at enhancing forecast accuracy. You will translate intricate data into practical insights that bolster business growth, efficiency, and supply chain robustness.

Key Responsibilities

• Develop and update statistical forecasting models for an 18-month horizon utilizing R, Python, and Google Cloud Platform to enhance demand predictions.
• Manage forecast and pricing data within SAP Integrated Business Planning, ensuring alignment and data integrity according to business needs.
• Collaborate with Marketing and other departments to construct and refine market demand models informed by diverse internal and external data.
• Create and implement machine learning applications to automate planning processes and increase operational efficiency.
• Analyze both structured and unstructured large datasets to detect trends, risks, and opportunities that provide actionable business intelligence.
• Convert complex business challenges into scalable and reliable analytical solutions by applying innovative problem-solving and thorough data expertise.
• Design dashboards, reports, and visualizations using Microsoft Power BI to convey insights effectively and support data-led decisions.
• Continuously assess and refine forecasting methods, the planning process, and analytic tools to create tangible business value.
• Work closely with teams in Demand Planning, Supply Chain, Commercial, and IT to propagate and implement analytical best practices throughout the company.

Candidate Traits

• Exceptional analytical and quantitative problem-solving capabilities, adept at transforming complicated data into meaningful business insights.
• Proven experience with time series forecasting including univariate, multivariate, and ensemble approaches.
• Proficient in Python and statistical modeling with a strong background in creating, launching, and maintaining analytics and machine learning solutions.
• Skilled in handling large, multifaceted datasets that include both structured and unstructured information.
• Solid business awareness paired with the ability to relate analytical results to business strategies and planning decisions.
• Experience in building scalable and maintainable data and analytics architectures within cloud infrastructures.
• Excellent communication skills with the capacity to articulate technical ideas clearly to diverse audiences.
• Strong teamwork abilities to effectively collaborate across various departments such as Marketing, IT, Commercial, and Supply Chain.
• Innate curiosity, innovative thinking, and a commitment to continual process enhancement driven by technology and data.
• Self-driven with the ability to juggle multiple priorities effectively in a dynamic environment.

Qualifications

• Bachelor's or Master’s degree in Statistics, Mathematics, Data Science, Computer Science, or related quantitative discipline.
• At least 2 years of hands-on experience developing analytical solutions using Python.
• Minimum 2 years’ exposure to time series forecasting and statistical forecasting models including univariate, multivariate, and ensemble techniques.
• Over 2 years of working with database systems such as SQL with capabilities in data modeling, querying, and management.
• At least 1 year of experience with cloud platforms like Google Cloud Platform, AWS, or Azure.
• More than 2 years of creating dashboards and visualizations using Microsoft Power BI or similar tools.
• Familiarity with machine learning, predictive analytics for supply chain or demand planning is advantageous.
• Knowledge of SAP Integrated Business Planning or other advanced planning systems is beneficial.

Success Metrics

• Delivery of consistently high-quality and accountable results.
• Proactive problem resolution and contribution of innovative ideas improving business outcomes.
• Strong collaboration and clear communication with stakeholders organization-wide.
• Continuous improvement and adoption of better processes, tools, and methods.
• Promotion of decisions driven by data through innovation and meticulous execution.
• Achieving measurable progress in forecast accuracy, planning efficiency, and overall business performance.