Job DescriptionWorldpay, LLC seeks Payment Optimization Data Scientist II in Cincinnati, OH to employ machine learning and statistical modeling to create and enhance data driven products.
What you will be doingThe Payment Optimization Data Scientist II will analyze and extract insights from internal and external data. Additionally, the role will:
- Work with big data and transform complex datasets into more usable formats.
- Work with a variety of data science tools and programming languages such as SAS, PYTHON, R, SCALA, SQL.
- Work independently and collaborate with other groups to solve complex problems.
- Create and present analyses to internal and external partners and clients.
- Document models and write code to track and monitor models and product performance.
- Understand the realities of model development and make pragmatic and business-aware choices when trading-off sophistication and accuracy versus implementation and performance costs.
- Perform other related duties assigned as needed.
RequirementsMaster's degree or foreign equivalent in Computer Science, Data Science, Computer Engineering, or related field and four (4) years of experience in the job offered or a related occupation:
- developing and deploying supervised machine learning models including CatBoost and XGBoost boosting/ensemble methods for Dynamic Transaction Retry, Time-of-Day optimization, and Intelligent Payment Orchestration to improve payment acceptance/approval rates and subscription recovery metrics;
- applying Multi-Armed Bandits Reinforcement Learning and Causal Inference techniques to production systems for continuous optimization and feature/data updating including measuring improvements via AUC scores and acceptance rate uplift;
- consolidating and normalizing disparate payment gateway response codes/ large-scale financial transaction data into consistent feature sets for ML (Machine Learning) models to improve model interpretability and predictive accuracy;
- performing segmentation analysis using unsupervised ML algorithms with K-Means and DBSCAN to enhance data quality and inform supervised models for chargeback prediction and risk mitigation;
- performing large-scale feature extraction and processing from relational and cloud-based systems with MySQL, BigQuery, & Snowflake and integrating these derived features into models running on Databricks/Apache Spark;
- designing, executing, and analyzing A/B tests and pre-post analyses, and utilizing time-series modeling techniques to monitor and mitigate data drift and model drift in high-frequency predictive systems;
- and translating technical findings into actionable, strategic recommendations for product development and executive stakeholders, ensuring the stability and profitability of global payment systems.
Telecommuting and/or working from home may be permissible pursuant to company policy. When not telecommuting, must report to work site.
What we offer you- A competitive salary and benefits
- A variety of career development tools, resources and opportunities
- The chance to work on some of the most challenging, relevant issues in the payment industry
- Time to support charities and give back in your community
