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# Principal - Data Science

- **Company:** Ally Financial Inc.
- **Location:** Detroit, MI, United States (Remote available)
- **Experience:** Expert
- **Salary:** $110,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Data Analysis, Big Data, Cloud Engineering, Software Quality, Code Review, Digital Assets, Python (Programming Language), Machine Learning, Backtesting, SAS (Software), SQL Databases, Git, Information Technology, Performance Monitor, Software Version Control, Data Pipelines
- **Published:** September 23, 2026
- **Apply:** https://www.detroitjobsite.com/job.asp?id=3401625726&tx=HT767TYI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

We're looking for a passionate Data Scientist with great communication skills for our . This person will conduct complex analytics projects, studies and experiments, and create data products to surface insights and drive business strategy related to . They will also mentor developing analytics professionals and connect with other analytics and data science teams to build capability. Do you have a focus on measurable business outcomes, strong analytical skills, and a desire to make an impact? If you are a creative and critical thinker, storyteller, learner and innovator and want to work in an environment focused on using data science and big data tech, keep reading.", * 7+ years of relevant experience

 * Bachelor's degree in quantitative/technical discipline (Math, Computer Science, Data Science/Analytics, Applied Statistics, etc.) or equivalent practical experience, * Significant experience (5+ years) in analytics, data science, machine learning, or applied statistics with a track record of independently delivering production-impacting work and measurable business impact.
 * Bachelor's or Master's degree in quantitative/technical discipline (Math, Computer Science, Data Science/Analytics, Applied Statistics, etc.) or equivalent practical experience.
 * Domain experience in financial services (preferably auto lending) focused on data/statistical analysis, credit risk, pricing, fraud, or portfolio optimization preferred.
 * Strong working knowledge of machine learning algorithms, AWS and cloud development tools, and Data Science/Analytics/BI methodologies.
 * Advanced skills in SQL, Python (R or SAS a plus); strong skills in exploratory data analysis, statistical model development, validation/testing, and operation (including performance monitoring).
 * Experience with Git/source control (branching, pull requests, code reviews) preferred.
 * Advanced knowledge of statistics and experiment design.
 * Ability to design, execute, and interpret controlled experiments with focus on translating findings into business decisions.
 * Excellent written and verbal communication skills; able to tailor narratives and technical details to executives, cross-functional partners, and engineering audiences.
 * Experience working with (and ability to influence) matrix partners.
 * Learning agility to rapidly understand a business, its context, and its value stream.
 * Proactive seeking of answers and takes ownership from problem framing through delivery; doesn't wait for direction to move work forward.
 * Works independently end-to-end and collaborates with partners as needed; adaptable to changing priorities and project needs.
 * Comfort working with ambiguous or sparse data; ability to design pragmatic solutions with imperfect inputs.
 * Outstanding analytical and problem-solving skills; critical thinker with strong attention to detail.
 * Ability to work both independently and collaborate as needed as project demands shift and change.

## Description

* Partner with strategy, operations, and business leaders to transform ambiguous questions into data-driven solutions that drive measurable outcomes.
 * Communicate findings and recommendations clearly to technical and non-technical audiences and influence decision-making at multiple levels by making holistic recommendations with considerations for risks, tradeoffs, and blind spots.
 * Design, execute, and interpret experiments and analyses to evaluate business strategy changes and quantify impact.
 * Build robust, scalable models and analytical solutions; document thoroughly; and implement automated monitoring aligned with Model Risk Management standards.
 * Benchmark challenger models and perform rigorous validation (back testing, stability, fairness, and drift) with monitoring and clear acceptance criteria.
 * Productionize models and analytics pipelines in collaboration with data and implementation teams, ensuring reliability, observability, and reproducibility.
 * Build reliable, documented data pipelines and features from enterprise sources; transform and curate datasets to support analysis, ad-hoc exploration, and model development.
 * Develop high-quality data assets: feature stores, standardized metrics definitions, actionable dashboards, and monitoring for model and data quality.
 * Own end-to-end project delivery: define problem statements, create project plans, align stakeholders, manage risks, and deliver outcomes on time.
 * Manage projects of various size/complexity, day-to-day tasks, and priorities independently.
 * Create strong partnerships with key partners, understand organizational goals, and shape roadmaps to support achieving them.
 * Establish and promote best practices in modeling, code quality, and experimentation; mentor via code reviews and knowledge sharing (without direct reports).
 * Proactively identify and learn new data sources, tools, methodologies, and approaches to improve performance and accelerate delivery.


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