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Job Title
Data Scientist

What does a great Data Scientist do?
The Risk Analysts in EPayments are the data scientists responsible for the identification of fraud behavior patterns, the estimation of predictive models and the installation of policies to reduce losses for our client banks. Our analysts are responsible for the full predictive modeling cycle, including data preparation, model estimation presentation to clients, installation of policies and performance tracking.

This position is an excellent opportunity to learn and develop modeling skills for the e-payment sectors, work in a large-date environment and apply statistical forecasting techniques. It also provides a high degree of interaction with internal decision makers and external stakeholders in the industry.

As a Risk Data Scientist, you can look forward to:

• Using statistical techniques to examine payment data provide measures for outlier identification on key measures such as transfer velocity, ip-distance profiling and device recognition.
• Creating the predictive measures for the patterns and collecting the information using SQL on ORACLE and Python/Spark data structures.
• Estimating predictive classification models to separate legitimate from fraudulent behavior, using XGBoost, LighGBM and neural network libraries.
• Installing the predictive model code in our systems and collecting performance both in ORACLE and Python and creating the policy for actions to be taken (manual review, live challenge using SMS, suspension etc...)
• Interacting with heads of the fraud-strategy departments at the client banks. Our analysts present the policies, document the models and negotiate approvals and timelines for implementation with the stakeholder at the bank.

Required Skills:

• Thorough understanding of statistics, confidence intervals and some background on CHAID, statistical distributions and estimation methods such as regression, binning confidence interval etc.
• Must have hands-on experience collecting data in SQL from data systems and summarize the attributes for the data in counters, sums, histograms and frequencies. Summarize information for distributions and finding quantiles and running totals.
• Hands on experience processing data in Python: Should be able to read a csv file, import the data, run counters and summary distribution, aggregate totals and do exploratory analysis on a data set.
• Should be able to estimate classification/ predictive models using the open-source library in Scikit learn for Python. Measure the goodness of fit and compare them across alternative models, such as ROC, gains charts.
• Should be able to measure changes in underlying distributions, and compare between different populations or policies.
• Document the analysis and present it verbally providing insights on the factors that are most relevant for the model and how can it be used to predict/manage a policy.

Basic Qualifications for Consideration

• Bachelor's Degree in related field
• 3-5 years analytics experience, with 1-2 years of risk modeling experience
• Strong experience with predictive analytics tools - Python/Spark/R/SQL

Preferred Qualifications for Consideration

• Client facing or consulting experience
• Previous e-commerce fraud prevention is a plus
• Experience with ML modeling tools such as Spark, H20, Hadoop, etc

Salary Range
$104,000.00 - $160,800.00

These pay ranges apply to employees in Sunnyvale, California. Pay ranges for employees in other states may differ.

It is unlawful to discriminate against a prospective employee due to the individual's status as a veteran.

This position is not eligible for an annual incentive opportunity and is also not eligible to earn commissions.

Thank you for considering employment with Fiserv. Please:

• Apply using your legal name
• Complete the step-by-step profile and attach your resume (either is acceptable, both are preferable).

Our commitment to Equal Opportunity:

Fiserv is proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, gender identity, sexual orientation, age, disability, protected veteran status, or any other category protected by law.

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