Senior Data Scientist

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Job Summary

The Data Science team delivers best in class actionable business insights through cutting-edge predictive models and data science techniques. The Senior Data Scientist is responsible for generating data-driven insight using advanced analytics and machine learning techniques to support enterprise data science needs within Bread Financial, and researching state-of-art advanced analytics techniques to continuously improve the Data Science teams competence within the analytics industry.

Essential Job Functions:

• Analytics – Extract and sample data, conduct data integrity checks and applicable data pre-processing such as treatment of missing values and outliers. Conduct exploratory data analysis for preliminary data insights to drive the selection of modeling approach that best addresses the business problem. Reveal hidden data patterns by data mining using unsupervised learning techniques such as clustering analysis and factor analysis. Conduct feature engineering to create/derive model predictors with strong predictive power. Train/tune classification/regression models by applying supervised learning techniques such as generalized linear models assuming applicable underlying distributions such as logit and gamma, tree-based models such as decision trees, random forest, boosted trees, etc., and neural net models. Conduct proper model test/validation, diagnose and fix model issues (e.g., over-fitting) when applicable. Size the impact of using the models in production as part of the current strategy. Present results and business case to manager. Provide support for implementation and monitoring of solutions that are implemented.

• Collaboration – Translate analytical results into useful recommendations for review with manager. Demonstrate strong verbal and written communication skills when working with internal partners and when presenting results to various audiences. Develop foundational knowledge of credit card operations, banking, financial, loyalty rewards, retail, and credit card regulations while working with the business. Collaborate with other data scientists in the organization to share best practices and data science innovations.

• Data Science Innovation – With direction from leader, research industry trends in data science of new tools, emerging algorithms, advanced platforms, and alternative data to enhance modeling effectiveness and efficiency. Conduct use case testing for new tools/techniques/platforms/data and provide user input/feedback. Conduct research to continuously improve predictive modeling methodology to achieve better outcomes.

• Model Risk Management - Develop foundational knowledge on common model risks and related regulatory requirements. Apply proper first line of defense controls during model development process to minimize model risk. Create comprehensive model governance documentation and archive model data, scripts, and results. Collaborate with model risk management partners to complete model validation/auditing. Complete remediation as required by model governance process.

Reports To:

Manager or higher

Direct Reports:

None

Working Conditions/ Physical Requirements:

• Normal office environment. Hybrid role.
• Some travel may be required.

Minimum Qualifications:

• Bachelor's degree in statistics, mathematics, engineering, data science, economics, computer science, or another quantitative field of study.
• 4+ years of experience in the software field.

Preferred Qualifications:

• Master's degree in statistics, mathematics, engineering, data science, economics, or computer science.
• Experience in developing statistical models/machine learning models and conducting data mining to solve business problems.
• Experience in extracting and processing large files/data sets.
• Experience interpreting model results and translating insights into business recommendations.

Knowledge, Skills and Abilities:

• Must:

• Data Analytics

• Data Mining
• Data Science
• Machine Learning
• Pivot Tables
• PowerPoint Presentations
• Predictive Modeling
• Python
• Spark SQL
• Statistical Concepts
• Structured Query Language (SQL)

• Good to Have:

• Financial Services

• Natural Language Processing (NLP)
• Cloud Experience (AZURE/AWS)
• Time Series Forecasting

Other Duties

This job description is illustrative of the types of duties typically performed by this job. It is not intended to be an exhaustive listing of each and every essential function of the job. Because job content may change from time to time, the Company reserves the right to add and/or delete essential functions from this job at any time.

About Bread Financial

At Bread Financial, you'll have the opportunity to grow your career, give back to your community, and be part of our award-winning culture. We've been consistently recognized as a best place to work nationally and in many markets and we're proud to promote an environment where you feel appreciated, accepted, valued, and fulfilled—both personally and professionally. Bread Financial supports the overall wellness of our associates with a diverse suite of benefits and offers boundless opportunities for career development and non-traditional career progression.

Bread Financial

(NYSE: BFH) is a tech-forward financial services company that provides simple, personalized payment, lending, and saving solutions to millions of U.S consumers. Our payment solutions, including Bread Financial general purpose credit cards and savings products, empower our customers and their passions for a better life. Additionally, we deliver growth for some of the most recognized brands in travel & entertainment, health & beauty, jewelry and specialty apparel through our private label and co-brand credit cards and pay-over-time products providing choice and value to our shared customers.

To learn more about Bread Financial, our global associates and our sustainability commitments, visit

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• All job offers are contingent upon successful completion of credit and background checks.
• Bread Financial is an Equal Opportunity Employer.

Job Family:

Data and Analytics

Job Type:

Regular