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# Senior Data Scientist I (Finance)

- **Company:** CPCS Transcom Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Data Analysis, Big Data, Data Visualization, Python (Programming Language), Machine Learning, NumPy, Oracle Fusion Middleware, Transaction Data, Pandas, Scikit Learn, Data Analytics, Non-relational Database, Software Coding, Data Pipelines
- **Published:** November 14, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-data-scientist-i-finance-ny--59e442a6-ebfd-47be-8e97-e46a56233809

## About the Role

Data Science, Finance, Algorithms, Analysis Skills, Forecasting, Cost Analysis, Machine Learning, Finance, Mentoring, Performance Modeling, * Expertise in developing and implementing complex machine learning and statistical models, ideally on financial or transactional data.

 * Proficient in preparing, cleaning, and transforming large datasets.
 * Strong coding skills in Python or R, with relevant libraries (e.g., scikit-learn, pandas, NumPy).
 * Ability to create clear, insightful data visualizations and dashboards for finance stakeholders.
 * Experience in designing, executing, and analyzing controlled experiments (A/B testing).
 * Excellent ability to articulate complex findings to technical and non-technical audiences and work cross-functionally.
 * Proficient in querying and managing data in relational and non-relational databases, including ERP sources such as Oracle Fusion.

## Description

* Design, develop, and implement statistical models and ML algorithms to solve finance and commercial problems (e.g., sales/cost forecasting, margin optimization, anomaly detection).
 * Conduct in-depth exploratory data analysis (EDA) to uncover trends, patterns, and actionable insights from large financial and transactional datasets.
 * Clean, transform, and validate data from Oracle Fusion and other sources to ensure data quality and integrity for modeling.
 * Build and maintain robust data pipelines and features for model training and inference.
 * Evaluate model performance using appropriate metrics, perform rigorous A/B testing, and continuously optimize models for production.
 * Collaborate with engineers, product owners, and Finance stakeholders to define requirements and integrate solutions.
 * Communicate complex analytical findings and model results clearly to both technical and non-technical finance audiences.
 * Research and apply cutting-edge data science techniques to improve existing methodologies and capabilities.
 * Develop and present data visualizations, dashboards, and reports to illustrate insights and track key financial metrics.
 * Mentor junior data scientists on best practices for analysis, modeling, and productionization.