Wish is a mobile e-commerce platform that flips traditional shopping on its head. We connect hundreds of millions of people with the widest selection of delightful, surprising, and—most importantly—affordable products delivered directly to their doors. Each day on Wish, millions of customers in more than 160 countries around the world discover new products. For our over 1 million merchant partners, anyone with a good idea and a mobile phone can instantly tap into a global market.
We're fueled by creating unique products and experiences that give people access to a new type of commerce, where all are welcome. If you’ve been searching for a supportive environment to chase your curiosity and use data to investigate the questions that matter most to you, this is the place.Job Description
At Wish, our Data Science & Engineering team is composed of Data Scientists, Data Analysts & Data Engineers who focus on centralizing corporate data in order to gain insights, knowledge and scalability, that empower a proactive and rigorous analysis of key business indicators. Our mission is to derive wisdom from data via the application of Data Science.
As a Machine Learning Engineer joining the Buyer Risk DSE team, you will help to develop systems and services to secure our platform and our users (eg. detecting and preventing account takeovers, establishing product features to proactively protect users, building out data pipelines to detect and prevent bad traffic from polluting data, promo abuse, fraudulent payments, etc). Ideally the person we are looking for is someone who was involved in these types of operations at scale and has hands-on knowledge.
What you'll be doing:
- Conceptualize, build and operationalize scalable infrastructure to support real time streaming of our models and rules for account security, transaction security, network analysis, anomaly detection, etc.
- Own feature engineering with data scientists, to build scalable, reliable ML pipelines for low latency, high throughput, high accuracy models and rules
- Own end to end model development and deployment, or support the tech lead for the development and deployment
- Audit the features being generated for reliability, scalability and backfill availability
- Ensuring data integrity of product analytics and ML models and rules from automated traffic
- Working with internal stakeholders and external vendors to identify any loopholes or vulnerabilities in current product features
- Master’s degree in Computer Science, or related fields. PhD preferred.
- 1+ years building successful consumer facing software products
- 1+ years building successful machine learning infrastructure
- 1+ years years with big data systems, such as Spark, Hadoop
- Computer Science fundamentals in object-oriented design, data structures, algorithm design, problem solving, and complexity analysis
- Machine learning fundamentals in statistical learning, accuracy metrics, loss functions, hyper-parameter tuning, feature engineering and ML system design
- Proficiency in at least one modern object-oriented programming language such as Python, Java or C++
- Full stack or backend engineering experience preferred, with strong system fundamentals
- Strong analytical ability to heavily utilize data insights in decision making.
- You enjoy taking end-to-end responsibilities, and ownership over a project.
- You enjoy communicating with stakeholders and people with different backgrounds.
- You always start from business problems, not technical details. Even better, you start from the press release of the final product, and the FAQ you might anticipate from customers, management and stakeholders.
Wish values diversity and is committed to creating an inclusive work environment. We provide equal employment opportunities for all applicants and employees. We do not discriminate based on any legally-protected class or characteristic. Employment decisions are made based on qualifications, merit, and business needs. If you need assistance or accommodation due to a disability, please let your recruiter know. For job positions in San Francisco, CA, and other locations where required, we will consider for employment qualified applicants with arrest and conviction records.