Sr Data Scientist - BMW Bank

BMW Group•Salt Lake City, UT

1d•$54,300 - $108,600•Hybrid

About The Position

BMW Bank of North America, LLC, a subsidiary of BMW Financial Services NA, LLC, opened in 1999 in Salt Lake City, Utah. It is an industrial bank that provides in-house banking services to BMW and MINI customers, dealerships, and affiliates, including direct and indirect automotive retail installment contracts and credit card services. Be a part of our exciting growth by expressing an interest in our Sr. Data Scientist - BMW Bank position located in Salt Lake City, Utah or Columbus, OH.

Requirements

Bachelor's degree or equivalent experience and/or certifications with a preferred area of study in Data Analytics, Statistics, Mathematics, Computer Science, Economics, or similar quantitative field.
Minimum of 2 years of professional experience as a data scientist or similar in a corporate environment; to include the following:
Developing, training, tuning, validating, and deploying machine learning models using Python, R, and related analytical libraries to solve business problems involving prediction, classification, optimization, pricing strategy, and decision support.
Formulating target variables, engineering predictive features, selecting appropriate algorithms, reducing false positives, and evaluating model performance using metrics such as accuracy, precision, recall, lift, error rates, and business impact measures.
Applying, interpreting, and explaining statistical and quantitative models, including regression, multilevel modeling, generalized linear models, Poisson regression, ARIMA/time-series methods, clustering, and other advanced analytical techniques to support data-driven decision-making.
Building pricing prediction models, optimizing model outputs, and estimating financial or operational impact through applied machine learning initiatives, including auction price optimization, predictive analytics, and model-based business strategy recommendations.
Testing model assumptions, analyzing outputs, measuring relationships among variables, and identifying key drivers to generate actionable recommendations for business, operational, financial, or strategic decisions.
Designing, building, orchestrating, automating, and maintaining data pipelines and analytical workflows using Python, SQL, Shell scripting, Hadoop, Hive, HBase, or similar big data technologies to process enterprise-scale datasets.
Extracting, transforming, validating, and curating high-volume structured and semi-structured datasets from enterprise data platforms to support reporting, machine learning, dashboarding, operational monitoring, and data quality controls.
Creating, visualizing, and delivering dashboards, KPI reports, analytical summaries, and business intelligence solutions using Tableau, Python, and other reporting tools to communicate trends, performance metrics, and operational insights.
Curating KPI reports, building Tableau dashboards, and presenting analytical findings to improve stakeholder visibility, support leadership decision-making, and translating complex data outputs into clear business narratives.
Gathering business requirements, translating analytical questions into technical solutions, communicating findings, and partnering with technical and non-technical stakeholders to deliver scalable, data-driven solutions across business, technology, and analytics teams.

Nice To Haves

Quantitative oriented Master’s Degree
3+ years of experience at a US federally regulated financial institution, ability to demonstrate knowledge of credit risk and model risk management regulations, guidance and best practices.
2+ years Financial Services or Automotive Industry experience.
2+ years participating as a member of project teams using agile or waterfall methodologies.
2+ years Data Governance / Management process application experience, i.e. Informatica EDC or equivalent training.
2+ years ETL architecture and design experience.
3-5 years of experience in IT

Responsibilities

Lead end-to-end data science initiatives, including data acquisition, cleansing, exploration, and analysis to generate actionable business insights.
Design, develop, and deploy predictive and prescriptive models using statistical methods and machine learning techniques to solve complex business problems.
Translate business needs into analytical solutions and communicate results through clear data storytelling and visualizations for both technical and non-technical stakeholders.
Partner with cross-functional teams and business stakeholders to identify opportunities, implement models, and drive data-driven decision making.
Serve as a subject matter expert in data science, model performance, and analytics, supporting governance, audits, regulatory requirements, and best practices.
Monitor, evaluate, and improve model performance, data quality, and analytical processes while contributing to tool selection, vendor management, and data governance initiative.
Provide strategic insights to improve revenue, reduce costs, optimize risk, and support business planning, including budgeting and performance tracking.