Machine Learning Engineer, Ecommerce Risk ControlTik Tok • San Jose, Arizona, United States
Machine Learning Engineer, Ecommerce Risk Control
Tik Tok
- San Jose, Arizona, United States
- San Jose, Arizona, United States
À propos
Employment Type: Regular
Job Code: 2WUV
Responsibilities
Invent, implement, and deploy state of the art machine learning algorithms, to respond to and mitigate business risks in Tiktok products/platforms.
Build prototypes and explore conceptually new solutions, define and conduct experiments to validate/reject hypotheses, and communicate insights and recommendations to Product and Tech teams.
Collaborate with cross‑functional teams from multidisciplinary science, engineering and business backgrounds to enhance current automation processes.
Develop efficient data querying infrastructure for both offline and online analysis, uncover evolving attack motion, identify weaknesses and opportunities in risk defense solutions, explore new space from the discoveries.
Define risk control measurements; quantify, generalize and monitor risk‑related business and operational metrics; align risk teams and stakeholders on risk control numeric goals, and promote impact‑oriented, data‑driven data science practices for risks.
Maintain technical documents and communicate results to diverse audiences with effective writing, visualizations, and presentations.
Qualifications Minimum Qualifications:
Master’s degree(s) in Computer Science, Mathematics, Machine Learning, or other relevant STEM majors (e.g., finance if applying for financial fraud roles).
Experience programming in Java, C++, Python, or related languages.
3+ years of hands‑on experience in building and delivering machine learning models for large‑scale projects.
Track record of developing and implementing models and visualizations using programming and scripting (Scala, Python, R, Ruby, and/or Matlab).
Experience using various forecasting, machine learning, and statistical tools and communicating results, plans and/or risks clearly.
Ability to think creatively and solve problems.
Preferred Qualifications:
A PhD in CS, Machine Learning, Statistics, Operations Research, or relevant field.
4+ years of industry experience in predictive modeling and analysis.
Experience collaborating with product, operations and engineering teams is a plus.
Excellent analytical and communication skills and ability to influence stakeholders.
Experience in e‑commerce/online companies in fraud/risk control functions.
Salary & Compensation The base salary range for this position in the selected city is $150,000 - $387,600 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 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.
Equal Employment Opportunity / Fair Chance 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.
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Compétences linguistiques
- English
Avis aux utilisateurs
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