Machine Learning Engineer (Data Mining) - Global E-Commerce
Responsibilities
About The Team
The e-commerce industry has seen tremendous growth in recent years and has become a hotly contested space amongst leading Internet companies, and its future growth cannot be underestimated.
With millions of loyal users globally, we believe TikTok is an ideal platform to deliver a brand new and better e-commerce experience to our users. Our product engineering team is responsible for building an e-commerce ecosystem that is innovative, secure and intuitive for our users. We are looking for passionate and talented people to join us as we drive the future of e-commerce here at TikTok.
Responsibilities
- Responsible for development of data-empowered capabilities for e-commerce platform, enable different e-commerce verticals to extract and apply key insights from oceans of data to meet their business needs.
- Data mining for e-commerce, building tagging system based on massive data, analysing user, traffic, e-commerce and other data, combining with business to improve GMV.
- Data analysis, mining, model specific productization; understanding of e-commerce business, able to work with PM to continuously optimize data products based on data-driven.
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- Develop data mining pipelines to extract data assets (eg. entity labels) from aggregated data and enable the extraction of actionable insights from large volumes of domain data.
- Design and development of various strategies, algorithms and machine learning techniques to improve the accuracy and coverage of extracted data assets.
- Analyze e-commerce data-related business problems and abstract them into data-related requirements that are then implemented to help resolve key business problems.
Qualifications
Minimum Qualifications
- B. Sc or higher degree in Computer Science or related fields from accredited and reputable institutions.
- 5 years experience in data structures and algorithms, familiar with one or more: machine learning, natural language processing, data mining.
- Familiar with one of the Python/Java/Go/C++ programming languages.
- Familiar with common data processing technologies (Hadoop, Spark, Hive, Storm, Flink), with demonstrable coding skills.
- Familiar with common data mining techniques and machine learning algorithms (GBDT, Xgboost, FM, DFM, etc.), and familiar with their principles and scope of application.
- Proficiency in data mining, machine learning tools and common frameworks (TensorFlow, Pytorch).
Preferred Qualifications
- Continuous learning, keeping up with the latest research directions and results in the field of machine learning, for the exploration of big data applications in various vertical areas.
- In-depth research experience in analysis and mining of user behaviour data and construction of user portraits.
- Data mining experience in vertical industries (e-commerce, finance, games, travel, real estate, etc.).
- Development and optimisation of advertising and recommendation algorithms.
Perks and Benefits
Health and Wellness
- Health Insurance
- Dental Insurance
- Vision Insurance
- HSA
- Life Insurance
- Fitness Subsidies
- Short-Term Disability
- Long-Term Disability
- On-Site Gym
- Mental Health Benefits
- Virtual Fitness Classes
Parental Benefits
- Fertility Benefits
- Adoption Assistance Program
- Family Support Resources
Work Flexibility
- Flexible Work Hours
- Hybrid Work Opportunities
Office Life and Perks
- Casual Dress
- Snacks
- Pet-friendly Office
- Happy Hours
- Some Meals Provided
- Company Outings
- On-Site Cafeteria
- Holiday Events
Vacation and Time Off
- Paid Vacation
- Paid Holidays
- Personal/Sick Days
- Leave of Absence
Financial and Retirement
- 401(K) With Company Matching
- Performance Bonus
- Company Equity
Professional Development
- Promote From Within
- Access to Online Courses
- Leadership Training Program
- Associate or Rotational Training Program
- Mentor Program
Diversity and Inclusion
- Diversity, Equity, and Inclusion Program
- Employee Resource Groups (ERG)
Company Videos
Hear directly from employees about what it is like to work at TikTok.