Experience in the financial fraud detection domain, including developing machine learning models for identifying fraudulent activity. (4 years)
Experience translating business stakeholder requirements into machine learning problem formulations, including defining key performance indicators (KPIs), evaluation metrics, and success criteria for model development projects. (4 years)
Experience in end-to-end design and development of machine learning solutions, from data collection, feature engineering, preprocessing, feature selection, research, model architecture, training, optimization, evaluation, model encoding, deployment to production, and maintenance. (4 years)
Experience automating end-to-end machine learning solutions, including automated data collection, model training, model encoding, and deployment to production environments. (4 years)
Experience with Python-based data science libraries, including scikit-learn, pandas, NumPy, and Keras, for preprocessing, training, and evaluating machine learning models. (4 years)
Experience working with large-scale tabular datasets in cloud-based data warehouse environments for feature engineering and analytics. (4 years)
Experience encoding and optimizing machine learning models for production deployment using serialization frameworks such as ONNX for cross-platform model inference. (4 years)
Experience in embedding representation methods for tabular data. (1 year)
Experience collaborating with cross-functional teams, including business strategy, engineering, data infrastructure, and data science teams to align machine learning model outputs with business objectives and integrate models into production decision systems. (4 years)
EOE, including disability/vets.
The base pay for this role will depend on where you work and the relevant experience and expertise you bring. The expected range of pay for this role by location is:
Primary Location | Pay Range
San Jose, CA | Salary: $196,833.00-333,500.00 per annum. 40 hours per week; M-F, 9:00 a.m. to 5:00 p.m.
Additional compensation for this role may include an annual performance bonus, equity, or other incentive compensation, as applicable.
Must be legally authorized to work in the U.S. without sponsorship.
Subsidiary
PayPal
Travel Percent
0
PayPal does not charge candidates any fees for courses, applications, resume reviews, interviews, background checks, or onboarding. When making an application directly, we will never ask you to share passwords, one-time passcodes (OTP), or verification codes. Any such request is a red flag and likely part of a scam. All communication regarding your application will come from official PayPal email domains. If you suspect fraudulent activity, please report it immediately. To learn more about how to identify and avoid recruitment fraud please visit https://careers.pypl.com/contact-us.
For the majority of employees, PayPal's balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations.