Statistical Demand Forecasting Intern

Your Mission

As our Statistical Demand Forecasting intern located in San Jose or Los Angeles, working at the leading edge of statistical forecasting, you will have the unique opportunity to shape the future of solar energy across diverse markets, embodying our mission to create a sustainable and energy-efficient world for future generations. As an intern, you will be reporting to the Director of Sales, Inventory, and Operations Planning (SIOP) and your work will be pivotal in matching solar panel supply with customer demand. Using a combination of historical data and industry trend forecasts, you will develop, test, and deploy statistical models that forecast demand for products across the solar industry. The position involves working with a dynamic team across the disciplines of planning, sales operations, customer operations, and manufacturing. The forecast models you develop will be used to drive business decisions on when and how many solar panels to produce, and where opportunities exist for further expansion.

Your internship will culminate in the presentation of a report that delivers forecast models specific to segments of the solar market (e.g., technology type, product group, country). These models will generate forecasts with greater measured accuracy than the empirical methods previously used.

Make Your Mark: How You Will Drive Change

• Use MS Excel and / or statistical forecast programming languages (e.g., R) to develop forecast models that are specific to segments of the solar market (e.g., technology type, product group, country)
  • Potential forecasting methodologies include exponential smoothing, ARIMA, and dynamic regression. Potential to expand into neural networks or machine learning if they demonstrate greater accuracy
• Measure the accuracy of these forecasts over one to 12 month forecast horizons based on historical actual data with a focus on greatest accuracy in the 3 – 6 month forecast horizons
• Identify industry forecast trends that are predictive and can be incorporated into the models to improve forecast accuracy
• Understand the dynamics of supply and demand within the solar industry and potential impact of price elasticity on demand
• Write and present a report that details why the specific forecasting methods were selected, summarize how they work, and parameters that can be tuned as more data becomes available to maintain or further improve accuracy

Your Toolkit: Skills That Make a Difference

Essential for your Success

We understand that everyone has followed unique career paths, gaining valuable knowledge along the way. Don't worry if you don't tick all the boxes – apply anyway! Your experience is more than just a list of technical skills.

• Masters student in Engineering, Mathematics, Computer Science, or Economics with a strong quantitative grasp
• Strong experience working with MS Excel
• Analytical, self-motivated, and self-directed
• Passion for learning and critical thinking
• Interest in renewable energy / solar

It would be Nice if You Have

• Experience / knowledge in statistical forecast modelling
• Experience / knowledge in R programming language
• Experience / knowledge in machine learning / AI