Applied Scientist, Recommendation, E-Commerce Alliance

$136,000 - $205,000/Yr

Tiktok - San Jose, CA

Full-time - Mid Level

San Jose, CA

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

About the position

TikTok is the leading destination for short-form mobile video, with a mission to inspire creativity and bring joy. The e-commerce alliance team is focused on serving merchants and creators within the e-commerce platform, aiming to meet business indicators for merchants while enhancing the creative efficiency of creators. This role is crucial in providing high-quality content and a personalized shopping experience for TikTok users, as well as developing efficient shopping tools at seller centers and promoting collaboration between merchants and creators. We are actively seeking an Applied Scientist to join our Global E-Commerce Alliance Team. This position is centered on the development and implementation of innovative machine learning solutions specifically tailored for our recommendation systems in the E-Commerce sector. The successful candidate will collaborate closely with cross-functional teams, providing expert insights and influencing critical decision-making across various areas of our business. The role requires a strong foundation in machine learning, deep learning, and data analysis, as well as the ability to communicate complex technical concepts effectively to diverse audiences. The Applied Scientist will be responsible for designing, developing, and deploying sophisticated machine learning algorithms to enhance the performance of our recommendation systems. This includes utilizing machine learning, natural language processing (NLP), and computer vision (CV) techniques to analyze real-world signals generated from products, creators, merchants, and e-commerce transactions. The candidate will also be tasked with formulating end-to-end machine learning models for recommendation systems, ensuring their efficient operation, and analyzing extensive datasets to extract meaningful insights that facilitate data-driven decision-making. Additionally, the role involves designing and executing experiments to optimize recommendation functions and boost user satisfaction, while adhering to stringent data governance and privacy protocols.

Responsibilities

• Collaborate with cross-functional teams to design, develop, and deploy sophisticated machine learning algorithms to enhance the performance of our recommendation systems.
• Utilize 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 large recommendation models in an 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.

Requirements

• 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.

Nice-to-haves

• 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.

Benefits

• 100% premium coverage for employee medical insurance, approximately 75% premium coverage for dependents, and a 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 plus 17 days of Paid Personal Time Off (PPTO) (prorated upon hire and increased by tenure) and 10 paid sick days per year.
• 12 weeks of paid Parental leave and 8 weeks of paid Supplemental Disability.
• Mental and emotional health benefits through EAP and Lyra.
• 401K company match, gym and cellphone service reimbursements.