Machine Learning Engineer - Demand Forecasting (TikTok Global E-commerce Supply Chain and Logistics)

Tiktok-posted 4 months ago
Seattle, WA
5,001-10,000 employees
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Our team is dedicated to enhancing clients' shopping experience and reducing operational costs in the supply chain and logistics of TikTok E-commerce by developing end-to-end algorithm capabilities using machine learning, operations research, data mining, and causal inference methods. At TikTok, we are building a global e-commerce platform that connects millions of users with their favorite brands and products. Our Supply Chain and Logistics team plays a critical role in ensuring smooth operations and customer satisfaction. As a Machine Learning Engineer focused on demand forecasting, you will contribute to the development and implementation of cutting-edge machine learning models and algorithms to optimize inventory management, supply chain efficiency, and enhance the overall customer experience.

  • Develop and implement end to end machine learning models and algorithms for demand forecasting in the context of TikTok's global e-commerce supply chain and logistics operations.
  • Collect, clean, and preprocess large-scale data sets to ensure data quality and suitability for forecasting purposes.
  • Collaborate with data scientists and domain experts to understand business requirements, identify key demand drivers, and incorporate relevant features into forecasting models.
  • Conduct exploratory data analysis to gain insights into demand patterns, trends, and seasonality factors that influence purchasing behavior.
  • Build scalable and efficient data pipelines to automate data preprocessing, model training, and prediction processes.
  • Evaluate and optimize the performance of existing time series forecasting models, and propose enhancements or alternative approaches to improve accuracy and robustness.
  • Collaborate with software engineers to integrate machine learning models into production systems and ensure reliable and timely delivery of forecasts.
  • Monitor model performance, identify anomalies, and develop proactive measures to address potential forecast errors or biases.
  • Stay up to date with the latest advancements in machine learning, demand forecasting techniques, and related domains, and apply this knowledge to enhance the team's capabilities.
  • Communicate findings, insights, and technical concepts effectively to both technical and non-technical stakeholders, fostering a collaborative and data-driven decision-making culture.
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