We are looking for a highly analytical and results-driven Forecasting Data Analyst to bridge the gap between advanced data science and supply chain business operations. In this hybrid role, you will contribute to the designing and deployment of forecasting models while directly supporting demand planning, inventory optimization, and operational decision-making. You’ll combine statistical modeling, machine learning, and business collaboration to deliver accurate and actionable forecasts that drive supply chain efficiency and resilience.
Key Responsibilities
- Analyze historical data, seasonality, trends, and external factors to improve forecast accuracy.
- Integrate forecasts into S&OP processes and provide insights to improve inventory planning and service levels.
- Monitor and improve forecast accuracy (e.g., MAPE, RMSE) and provide root-cause analysis for forecast errors.
- Communicate technical findings and forecasts clearly to both technical and non-technical stakeholders.
- Contribute to building and maintaining time series and machine learning models to forecast demand, sales, and other key supply chain metrics.
- Collaborate with cross-functional teams (supply chain, operations, finance, sales, marketing) to gather inputs, validate assumptions, and establish process that align forecasts with business goals.
- Contribute to development of automated data pipelines, dashboards, and tools to streamline forecast reporting and decision-making.
- Perform scenario analysis and risk modeling utilizing Maestro / Kinaxis to support supply chain contingency planning
- Continuously look for opportunities to improve processes and recommend data-driven solutions to enhance business performance.
- Support day to day activities including performing forecast reviews, ad-hoc data pulls and analysis, and investigations into chronic forecast errors.
Qualifications
Required:
- Bachelor’s or Master’s degree in Data Science, Statistics, Supply Chain Management, Industrial Engineering, or related field.
- 1–3 years of experience in forecasting, demand planning, or supply chain analytics or equivalent experience through school projects and/or internship programs.
- Sufficient knowledge in Python or R for statistical modeling and data manipulation.
- Familiar with forecasting techniques (ARIMA, Prophet, exponential smoothing, regression, etc.).
- Experience with data tools like SQL, Excel, and BI platforms (Power BI, Tableau).
- Familiarity with forecasting and supply chain planning systems (e.g., SAP IBP, Kinaxis, Oracle Demantra).
- Strong analytical mindset with attention to detail and problem-solving skills.
- Excellent communication and collaboration skills.
Preferred:
- Experience in manufacturing, retail, logistics, or similar industries.
- Exposure to machine learning frameworks (scikit-learn, XGBoost, TensorFlow).
- Knowledge of cloud platforms (AWS, GCP, Azure) and version control (e.g., Git).
- 2–4 years of experience in forecasting, demand planning, or supply chain analytics.
