Data Scientist II

GM Financial•Fort Worth, TX

1d•Hybrid

About The Position

The Data Scientist II is responsible for implementing the design, development, deployment, and maintenance of predictive/prescriptive/statistical models in originations, collections, risk, pricing, fraud, customer experience and marketing. Modeling with expertise in forecasting, optimization, data mining, analysis, and analyzing complex datasets; Conducting studies with the use of descriptive and supervised machine learning methods and advanced statistical methods using innovative and the latest advanced technique and algorithms; Summarizing, reporting, and providing polished presentations of findings to a variety of internal clients as well as working with other departments to achieve the overall company objectives; Lead in the production of research and analysis to quantify the impact of internal and external environments on portfolio performance.

Requirements

Ability to identify and understand business issues and map these issues into quantitative questions
Advanced knowledge and demonstrated understanding of applied methodologies including least squares regression, logistic regression, sampling methodologies, time series, survival analysis, cluster analysis, categorical data analysis, decision trees, multivariate methodologies, non-parametric techniques, principal components, and linear programming techniques
Advanced skills in Python, SAS, SQL, R, JMP, Excel, Word, PowerPoint
Ability to design and implement model documentation and monitoring protocols
Demonstrated understanding and experience with technical systems, datasets, data warehouses, and data analysis techniques
Efficiently work with large datasets
Strong quantitative, analytical and data interpretation skills with a solid foundation of mathematics, probability, and statistics
Strong written and verbal presentation skills with an ability to communicate effectively with Senior Management by making complex concepts easy to understand
Ability to be curious, ask questions, explore, and be creative when analyzing data and business problems
Ability to identify and seek needed information/research skills
Analytical thinking skills
Ability to interact collaboratively with internal and external customers
Capable of managing multiple and varied projects, including the ability to coordinate and balance numerous tasks in a time-sensitive environment, under pressure
Strong problem solving skills
2-4 years as Data Scientist or similar quantitative field required
High School Diploma or equivalent required
Master’s Degree or PhD in Statistics, Applied Mathematics, Econometrics, Economics, Operations Research, Industrial Engineering, Computer Science, Physics or similar quantitative field required (a combination of education, certifications, and/or work experience may be considered in lieu of an advanced degree)

Nice To Haves

Working effectively within an AI enabled environment: Ability to use AI tools (e.g., Microsoft Copilot) to support daily work
Skills in evaluating AI outputs for accuracy, compliance, and bias
Experience integrating AI into workflows to improve efficiency or insights
Familiarity with AI assisted research, summarization, and content generation
Understanding of responsible AI use, including ethics and data protection

Responsibilities

Performs research, analysis, and modeling on organizational data
Assists in analyzing key metrics and performing data analysis
Builds technical knowledge to support research and analytic responsibilities including advanced techniques and algorithms
Conducts research projects, incorporate project design, data collection and analysis, summarizing findings, developing recommendations and effectively communicating to leadership the impact to the business
Develops and applies algorithms or models to key business metrics with the goal of improving operations or answering business questions
Presents findings and analysis for use in decision making
Ensures that the delivered products meet the business needs of the company
Partners with and provide recommendations to business leadership on the appropriate application of analytics to business strategies and effectively communicate analysis and implications to senior leadership
Prioritizes tasks and meets project deadlines in a fast paced work environment.