Data Scientist - Fraud
Compensation: $86,306.67 - $153,250.00 /year *
Employment Type: Full-Time
Industry: Information Technology
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Radial is looking for an experienced senior data scientist to join our advanced analytics and data science team as a Machine Learning Data Scientist. In this role, you will be expected to work with our sr. manager of advanced analytics and data science as well as our fraud rules, fraud review team, technology, product, policy, and client relationship partners to build, deploy, and maintain advanced analytical solutions with the goal of reducing fraud charge backs, reducing false positive decline, improving client experience, and ensuring that Radial minimizes its total cost of fraud while minimizes friction to the merchant and consumer. Some tasks that this role may be responsible for include (but are not limited to):
- Conduct exploratory data analysis and unsupervised machine learning to identify fraud trend, segment and clusters, and optimization opportunity.
- Build, deploy, and maintain machine learning classification models for detecting fraud on real time transactions. Machine learning algorithms will be exercised are Logit, probit, complementary log-log regression, Random Forest, GBMs such as XGBoost, AdaBoost, CatBoost, LightGBM, RusBoost, AveBoost, ORBoost, SMOTEBoost, etc., Support Vector Machines, KNNs, MLP Neural Net, Convolutional Neural Net.
- Build, deploy, and maintain anomaly detection models such as iForest, Local Outlier Factor, GMM, one class SVM, etc.
- Develop end to end multistage fraud detections analytic engine consists of supervised, unsupervised, and semi supervised models.
- Manage machine learning model life cycle through model audit, back testing, forward testing, benchmarking with the help of performance metrics.
- Conduct risk reward analysis for different portfolio segment to identify strategic opportunities of portfolio enhancement.
- Conduct Social Network Link analysis to find deeply-connected first- and third-party fraud networks.
- Robotic process automation to reduce the need for manual, repeatable processes.
In addition, the role will be expected to work with the other data scientists and the team executive and stakeholders to help develop the data strategy for client protection to ensure the organization has the proper data to make the right decisions, with a priority on data availability in real-time, and generating true customer-level views able to make intelligent fraud decisions leveraging the entirety of our interactions with a customer.Required Skills
: "Must" have these skills to be minimally qualified.
- PhD or Master's degree in a quantitative discipline such as mathematics, statistics, engineering, economics, finance, and, computer science with strong knowledge of probability theory. PhD is preferred. In lieu of a specific degree, advanced certifications in combination with strong experience will also be considered.
- The candidate must be at an advanced to expert level with Python, R, and Spark with proficient-or-better skills expected in SAS and SQL.
- The candidate must be expert at building machine learning models leveraging both batch and online data.
- The candidate must be familiar with cloud computing such as MS Azure, AWS and server less lambda infrastructure.
- Must have 3+ years' experience with data science, with preference working in financial services. Strong preference to experience in fraud or cybersecurity.
- Candidate must have a proven track record of building and deploying analytical solutions that have resulted in material financial results and extensive management experience. Ability to work in a fast-paced, dynamic environment is critical. Must have exceptional organizational, multiple project management and communications skills.
Solid knowledge of Python, R, Java or Spark, and various commercial and model generation software. Should additionally have familiarity with other tools such as HUE, Hive, and other data gathering tools.
Associated topics: circuit, c#, electrical engineering, information technology, java, linux, optical, programmer, schematic, software engineer
* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.
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