Applied Scientist, Recommendation, E-Commerce Alliance

Responsibilities

The e-commerce alliance team aims to serve merchants and creators in the e-commerce platform to meet merchants' business indicators and improve creators' creative efficiency. By cooperating with merchants and creators, we aim to provide high-quality content and a personalized shopping experience for TikTok users, create efficient shopping tools at seller centers, and promote cooperation between merchants and creators.

We are actively seeking an Applied Scientist to join our Global E-Commerce Alliance Team. This role is centered on developing and implementing innovative machine learning solutions for our recommendation systems in E-Commerce business. The successful candidate will work closely with cross-functional teams, providing expert insight and influencing critical decision-making across multiple areas of our business.

Responsibilities:

- Collaborate with cross-functional teams to design, develop, and deploy sophisticated machine learning algorithms to enhance the performance of our recommendation systems.
- Utilize the ML, NLP, and CV techniques to deal with real-world signals generated from products, creators, merchants, e-commerce transactions, and so on.
- Design and deploy the large recommendation model, in the online learning manner, to serve billions of queries and products.
- Formulate end-to-end machine learning models for recommendation systems, ensuring their efficient and effective operation.
- Analyze extensive, complex datasets to extract meaningful insights, identify opportunities for improvement, and facilitate data-driven decision-making.
- Design and execute experiments, testing and iterating on machine learning models to optimize recommendation functions and boost user satisfaction.
- Stay abreast of the latest advances in machine learning and recommendation systems, integrating this knowledge into your work.
- Clearly communicate complex technical concepts, methodologies, and results to a diverse audience, influencing decisions based on your findings.
- Adhere to stringent data governance and privacy protocols, ensuring all user data is handled responsibly and ethically.

Qualifications

Minimum Qualifications
- PhD or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
- Solid experience in machine learning, deep learning, data mining, or artificial intelligence.
- Proficient in programming languages such as Python, C++, Java, or similar.
- Deep understanding of recommendation algorithms and personalization systems.
- Excellent problem-solving and analytical skills.
- Strong ability to communicate complex ideas effectively to both technical and non-technical audiences.

Preferred Qualifications
- Experience with reinforcement learning techniques.
- Proven modeling/algorithms competition records on Kaggle or top conferences' challenges.
- Proven programming competition records on ICPC, IOI or USACO.
- Experience working with recommendation systems, computational advertising, search engine, E-commerce recommendation systems.
- Publications in machine learning or related conferences or journals are highly desirable.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $162000 - $316800 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

Responsibilities

Responsibilities

The e-commerce alliance team aims to serve merchants and creators in the e-commerce platform to meet merchants' business indicators and improve creators' creative efficiency. By cooperating with merchants and creators, we aim to provide high-quality content and a personalized shopping experience for TikTok users, create efficient shopping tools at seller centers, and promote cooperation between merchants and creators.

We are actively seeking an Applied Scientist to join our Global E-Commerce Alliance Team. This role is centered on developing and implementing innovative machine learning solutions for our recommendation systems in E-Commerce business. The successful candidate will work closely with cross-functional teams, providing expert insight and influencing critical decision-making across multiple areas of our business.

Responsibilities:

- Collaborate with cross-functional teams to design, develop, and deploy sophisticated machine learning algorithms to enhance the performance of our recommendation systems.
- Utilize the ML, NLP, and CV techniques to deal with real-world signals generated from products, creators, merchants, e-commerce transactions, and so on.
- Design and deploy the large recommendation model, in the online learning manner, to serve billions of queries and products.
- Formulate end-to-end machine learning models for recommendation systems, ensuring their efficient and effective operation.
- Analyze extensive, complex datasets to extract meaningful insights, identify opportunities for improvement, and facilitate data-driven decision-making.
- Design and execute experiments, testing and iterating on machine learning models to optimize recommendation functions and boost user satisfaction.
- Stay abreast of the latest advances in machine learning and recommendation systems, integrating this knowledge into your work.
- Clearly communicate complex technical concepts, methodologies, and results to a diverse audience, influencing decisions based on your findings.
- Adhere to stringent data governance and privacy protocols, ensuring all user data is handled responsibly and ethically.

Qualifications

Qualifications

Minimum Qualifications
- PhD or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
- Solid experience in machine learning, deep learning, data mining, or artificial intelligence.
- Proficient in programming languages such as Python, C++, Java, or similar.
- Deep understanding of recommendation algorithms and personalization systems.
- Excellent problem-solving and analytical skills.
- Strong ability to communicate complex ideas effectively to both technical and non-technical audiences.

Preferred Qualifications
- Experience with reinforcement learning techniques.
- Proven modeling/algorithms competition records on Kaggle or top conferences' challenges.
- Proven programming competition records on ICPC, IOI or USACO.
- Experience working with recommendation systems, computational advertising, search engine, E-commerce recommendation systems.
- Publications in machine learning or related conferences or journals are highly desirable.