Machine Learning Engineer, Pricing - TikTok E-commerce Global Supply Chain and Logistics

$129,200 - $194,750/Yr

Tiktok - Seattle, WA

Full-time - Mid Level

Seattle, WA

Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services

About the position

TikTok is the leading destination for short-form mobile video, and our mission is to inspire creativity and bring joy. The E-commerce Global Supply Chain and Logistics team is dedicated to enhancing clients' shopping experience and reducing logistics operational costs in TikTok E-commerce. We are currently seeking a talented Machine Learning Engineer with a deep understanding of machine learning (ML), operations research (OR), data mining, and statistical inference. This position can be fulfilled in our San Jose and Seattle offices. In this role, you will be responsible for building a global logistics and warehousing network, improving operational efficiency, and reducing operational costs through data analysis, machine learning, and operations research methods. You will create a supply chain data portrait and knowledge graph across various dimensions such as vendors, commodities, place of origin, inventory, production capacity, and quality of fulfillment. Additionally, you will establish data-driven control-adjustment methods that enhance operational outcomes and user experience. Your work will involve highlighting global e-commerce trends to optimize commodity supplies, forecast demand, recommend vendor stocking, and enhance production capacity. You will design optimization algorithm strategies for order allocation systems, estimate the probability of core decision variables, and build simulation capabilities to continuously improve estimation accuracy. Furthermore, you will collaborate with upstream and downstream partners to optimize subsidy pricing and achieve the best price for commodity/freight pricing in multiple modes for domestic and cross-border scenarios. You will also model the probability estimation problems of logistics costs/benefits and price elasticity, continuously improving accuracy through machine learning and causal inference methods. A deep understanding of the e-commerce business scenario will be essential to refine logistics operation mode optimization, reduce logistics costs, and improve service quality through data mining. Participation in product design and collaboration with product, operations, and management teams will be crucial to promote the productization and implementation of algorithm models that empower the business.

Responsibilities

• Build global logistic and warehousing network, improve operations efficiency and reduce operational cost with data analysis, machine learning and operation research methods.
• Create supply chain's data portrait and knowledge graph in various dimensions such as vendors, commodity, place of origin, inventory, production capacity and quality of fulfillment.
• Establish data-driven control-adjustment methods that enhance operational outcomes and user experience.
• Highlight global e-commerce trends to optimize e-commerce commodity supplies, forecast commodity demand, recommend vendor stocking and enhance production capacity.
• Design optimization algorithm strategies for order allocation systems, able to meet diverse goals and constraints.
• Estimate the probability of core decision variables in the order allocation system and build simulation capabilities, continuously improving estimation accuracy.
• Design optimization algorithm strategies for commodity/freight pricing in multiple modes for domestic/cross-border scenarios, collaborating with upstream and downstream partners to optimize subsidy pricing and achieve the best price.
• Model the probability estimation problems of logistics costs/benefits, price elasticity, etc., and continuously improve accuracy through machine learning and causal inference methods.
• Deeply understand the e-commerce business scenario, and refine the direction of logistics operation mode optimization, logistics cost reduction, and service quality improvement through data mining.
• Participate in product design, work closely with product, operations, and management teams to promote the productization and implementation of algorithm models to continuously empower business.

Requirements

• Solid foundation in data structure and algorithm design.
• Proficient in using one of the programming languages such as Python, Java, C++, R, etc.
• Familiar with common machine/deep learning, causal inference, and operational optimization algorithms, including classification, regression, clustering methods, as well as mathematical programming and heuristic algorithms.
• Familiar with at least one framework of TensorFlow / PyTorch / MXNet and its training and deployment details, as well as training acceleration methods such as mixed precision training and distributed training.
• Strong practical ability; winners in Kaggle, COCO, ImageNet, NOI/IOI and other competitions are preferred, and those who have papers published in relevant competitions and top academic conferences (such as CVPR, ICCV, ECCV, ACL, EMNLP, etc.) are preferred.
• Familiar with big data related frameworks and applications; those who are familiar with MR or Spark are preferred.

Benefits

• 100% premium coverage for employee medical insurance
• Approximately 75% premium coverage for dependents
• Health Savings Account (HSA) with a company match
• Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life and AD&D insurance plans
• Flexible Spending Account (FSA) Options like Health Care, Limited Purpose and Dependent Care
• 10 paid holidays per year
• 17 days of Paid Personal Time Off (PPTO)
• 10 paid sick days per year
• 12 weeks of paid Parental leave
• 8 weeks of paid Supplemental Disability
• Mental and emotional health benefits through EAP and Lyra
• 401K company match
• Gym and cellphone service reimbursements