Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience in program or project management.
- Experience in developing forecasting models and scripting in SQL.
- Experience in supply chain logistics.
- 5 years of experience managing cross-functional/team projects.
- 5 years of experience applying statistical modeling or machine learning techniques to solve business problems.
- 5 years of experience developing/implementing forecasting models for logistics, supply chain, or related field, improving forecast accuracy and driving business impact.
- Experience with logistics planning and forecasting across warehousing, labor, and transportation, with strong problem-solving skills.
- Experience with logistics constraint modeling and integration with forecasting processes to generate feasible operational plans.
- Expertise in logistics network design and optimization, encompassing warehouse location analysis, logistics cost simulation, inventory optimization, transportation network design, and distribution center planning.
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About the job
A problem isn't truly solved until it's solved for all. That's why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Program Manager at Google, you'll lead complex, multi-disciplinary projects from start to finish - working with stakeholders to plan requirements, manage project schedules, identify risks, and communicate clearly with cross-functional partners across the company. Your projects will often span offices, time zones, and hemispheres. It's your job to coordinate the players and keep them up to date on progress and deadlines.
Our goal is to build a Google that looks like the world around us - and we want Googlers to stay and grow when they join us. As part of our efforts to build a Google for everyone, we build diversity, equity, and inclusion into our work and we aim to cultivate a sense of belonging throughout the company.
In this role, you will partner with our operations teams to develop and implement forecasting solutions that optimize logistics planning across Google Cloud's supply chain. You will build and enhance models that improve our ability to predict key operational needs, from warehouse capacity to transportation and labor. Your focus will be on driving forecast accuracy, improving the transparency of our forecasting methods, and integrating operational constraints into our planning processes, while also contributing to the longer terms strategy of our logsitics network. You'll ensure our warehouse network scales effectively, adapts to the evolving needs of the business, and delivers speed, efficiency, and predictability across our global operations.
Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.
The US base salary range for this full-time position is $139,000-$207,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
Responsibilities
- Design, build, and maintain sophisticated logistics forecasting models using a variety of techniques, including statistical modeling, machine learning (e.g., time series analysis, regression, classification), and simulation.
- Monitor and track key forecast accuracy metrics (e.g., MAPE, RMSE, Bias) to identify areas for improvement and ensure alignment with business objectives. Conduct root cause analysis of forecast errors to understand drivers of inaccuracy and implement corrective actions.
- Develop and implement strategies to continuously improve forecast accuracy and reduce forecast bias, while enhancing the explainability of forecasting models and outputs for both technical and non-technical audiences.
- Improve existing forecasting models by incorporating new data sources, refining algorithms, enhancing model architecture, and exploring new forecasting methodologies.
- Forecast warehouse space requirements based on predicted demand and inventory levels. Analyze performance of the existing warehouse network, identify bottlenecks, and evaluate potential improvements.