Payment Optimization Data Scientist II

About the role

We’re seeking a Payment Optimization Data Scientist II in Cincinnati, OH to join our team. This role involves analyzing and extracting insights from internal and external data, working with big data, and creating and presenting analyses to internal and external partners and clients.

Responsibilities

• 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.
• 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.

Requirements

• Master’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.
• Develop and deploy 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.
• Apply 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.
• Consolidate and normalize 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.
• Perform 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.
• Design, execute, and analyze A/B tests and pre-post analyses, and utilize time-series modeling techniques to monitor and mitigate data drift and model drift in high-frequency predictive systems.
• Translate technical findings into actionable, strategic recommendations for product development and executive stakeholders, ensuring the stability and profitability of global payment systems.

Qualifications

Telecommuting and/or working from home may be permissible pursuant to company policy. When not telecommuting, must report to work site.

Benefits

You will receive a competitive salary and benefits, a variety of career development tools, resources and opportunities, and 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 is also available.

Skills

The ideal candidate will have expertise in machine learning, statistical modeling, and data science tools such as SAS, PYTHON, R, SCALA, SQL, and proficiency in big data technologies like Databricks/Apache Spark.

Benefits

The company offers a range of benefits including a competitive salary, benefits package, career development opportunities, and the chance to work on significant payment industry challenges. Employees also have the opportunity to volunteer and give back to their communities.

Pay

Details on pay are not specified in the job posting.

Schedule

Details on schedule are not specified in the job posting.