Data Scientist Lead - Chief Data Scientist within Payment Testing Technology

The Payment Testing Technology team serves the Commercial clients of JPMorgan.  The team facilitates internal and client testing efforts to validate new payment product flows and updates to existing payment products in a production parallel environment.  On a daily basis, our clients move hundreds of billions of dollars. Thus testing of payment flows is critical in ensuring seamless payments transactions for the clients.  The team is responsible for test environment management and automation solutions for 60+ applications in the payments flow.

We currently serve 30,000 clients, some of which are the largest companies in the world. We provide an end to end payment testing experience for all payment products. We provide services to clients in all regions globally – Asia Pacific, Europe and Middle East, North America and Latin America.   

We’re looking for someone with considerable experience manipulating data sets and building statistical models, has a Master’s or PhD in Statistics, Mathematics, Computer Science or another quantitative field.

Job Responsibilities:

1. Strong Programming skills using Python, Java, Java Scripts


2. Strong in Statistics and probability: very well versed with 
   
   
   
   1. Probability distributions
   
   
   2. Over and under sampling
   
   
   3. Bayesian and frequentist statistics
   
   
   4. Dimension reduction
   
   
   5. Linear regression
   
   
   6. Clustering
   
   
   7. Decision Trees
   
   


3. Strong in Data wrangling and database management which involves  process of cleaning and organizing complex data sets to make them easier to access and analyze. Manipulating the data to categorize it by patterns and trends, and to correct and input data values can be time-consuming but necessary to make data-driven decisions.


4. Develop custom ML models and algorithms to apply to data sets and hands on experience in building various ML models like:
   
   
   
   1. Linear regression
   
   
   2. Logistic regression
   
   
   3. Naive Bayes
   
   
   4. Decision tree
   
   
   5. Random forest algorithm
   
   
   6. K-nearest neighbor (KNN)
   
   
   7. K means algorithm
   
   
   8. Ensemble models
   
   
   9. Simulation
   
   
   10. Scenario Analysis
   
   


5. Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, etc.


6. Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.


7. Develop company A/B testing framework and test model quality.


8. Coordinate with different functional teams to implement models and monitor outcomes.


9. Develop processes and tools to monitor and analyze model performance and data accuracy.


10. Experience in Fine tuning the model

Required Qualifications:

• Considerable experience manipulating data sets and building statistical models.


• Master’s or PhD in Statistics, Mathematics, Computer Science or another quantitative field