Job Vacancy
Hey data enthusiasts! If you’re on the hunt for a role that lets you crunch numbers, build models, and shape the future of finance from the comfort of your own space, this one is for you. GM Financial is calling all senior data scientists to join their dynamic team.
Picture yourself in a hybrid remote setting, where you can balance the freedom of working from home with the energy of in‑office collaboration. Whether you’re based in Fort Worth, TX or Detroit, MI, you’ll be part of a global organization that values innovation and impact.
Senior Data Scientist at GM Financial is more than just a title—it’s a chance to influence financial products that millions rely on. The role offers a flexible hybrid remote schedule, giving you the autonomy to choose where you work best while staying connected with teammates across two key U.S. hubs. This position is ideal for professionals who thrive on solving complex problems, deploying machine learning solutions, and translating data insights into actionable strategies. With a supportive culture, competitive compensation, and the chance to work on cutting‑edge projects, you’ll find a career path that rewards curiosity and expertise alike.
| Position | Senior Data Scientist |
| Company Name | GM Financial |
| Location | Hybrid Remote (Fort Worth, TX & Detroit, MI) |
| Salary | Negotiable |
About the Role
This position sits at the intersection of data science and financial services. You will lead projects that uncover patterns, predict trends, and drive decision making across GM Financial’s portfolio. Your work will shape lending strategies, risk assessment models, and customer engagement tools.
The role demands a blend of technical mastery, business insight, and collaborative spirit. You’ll partner with data engineers, product managers, and domain experts to turn raw data into meaningful outcomes.
- Design and deploy predictive models that improve loan approval accuracy.
- Develop dashboards that provide real‑time insights to stakeholders.
- Guide data governance practices to maintain quality and compliance.
Why GM Financial?
GM Financial is a leader in automotive financing, serving millions of customers worldwide. Working here means you’re part of a legacy of innovation and customer focus.
They value data‑driven thinking and encourage experimentation. The culture rewards those who challenge the status quo and push for measurable results.
- Industry‑leading data infrastructure.
- Opportunities to work on high‑impact projects.
- Strong emphasis on professional growth and mentorship.
Hybrid Remote Lifestyle
Hybrid remote means you can split your time between home and office. This flexibility supports work‑life balance while preserving team cohesion.
Expect to spend a portion of each week in Fort Worth or Detroit, depending on where you’re based, and the rest working from a location of your choice.
- Flexible scheduling to accommodate personal commitments.
- Regular virtual check‑ins to stay connected with colleagues.
- On‑site collaboration for brainstorming sessions.
Location Highlights
Fort Worth, TX, offers a vibrant tech scene, affordable living, and a welcoming community for data professionals.
Detroit, MI, is a city of revitalization, rich culture, and growing opportunities in automotive finance.
- Fort Worth: proximity to Dallas, a growing startup ecosystem.
- Detroit: historic ties to automotive innovation, access to talent pools.
Fort Worth, TX
Fort Worth is known for its blend of tradition and modernity. The city hosts a thriving community of data scientists, providing networking events and meetups.
With a lower cost of living than many tech hubs, you can enjoy a high quality of life while advancing your career.
- Access to local universities for talent sourcing.
- Active data science community events.
- Affordable housing options.
Detroit, MI
Detroit is a city that has reinvented itself, making it a fertile ground for tech innovation. The automotive roots align well with GM Financial’s mission.
Living in Detroit offers a unique blend of culture, history, and modern amenities.
- Rich cultural scene with museums and music venues.
- Growing tech startup presence.
- Strong support for remote work infrastructure.
Key Responsibilities
As a Senior Data Scientist, you’ll own the end‑to‑end data science lifecycle. From data extraction to model deployment, your work will directly influence business outcomes.
You’ll also mentor junior analysts, share knowledge, and set standards for analytical excellence.
- Gather and clean large datasets from multiple sources.
- Build machine learning pipelines using Python or R.
- Interpret model results and translate them into business recommendations.
- Collaborate with cross‑functional teams to define metrics.
Data Exploration and Feature Engineering
Start by understanding the data landscape. Identify key variables that drive customer behavior and risk.
Feature engineering will be crucial to model performance. Experiment with transformations and interaction terms to uncover hidden signals.
- Use pandas for data wrangling.
- Apply domain knowledge to create meaningful features.
- Document feature importance for future reference.
Model Development and Validation
Choose the right algorithms for each problem. Validate models using cross‑validation and performance metrics such as AUC, RMSE, or F1 score.
Ensure models are explainable to satisfy regulatory and business stakeholders.
- Implement XGBoost, LightGBM, or neural networks as needed.
- Use SHAP values for interpretability.
- Maintain reproducible code with version control.
Required Skills & Experience
To thrive in this role you’ll need a solid foundation in statistics, programming, and domain knowledge.
Experience with production‑grade model deployment and a passion for continuous learning will set you apart.
- 10+ years in data science or analytics.
- Proficiency in Python, SQL, and ML frameworks.
- Strong knowledge of financial products and risk metrics.
- Experience with cloud platforms such as AWS or Azure.
Programming Mastery
Python is the primary language. Familiarity with libraries like scikit‑learn, TensorFlow, and PySpark is essential.
SQL skills are required for data extraction and transformation tasks.
- Write clean, modular code.
- Leverage version control with Git.
- Automate workflows using Airflow or similar tools.
Statistical and ML Expertise
Deep understanding of statistical theory and machine learning concepts will guide your model choices.
You should be comfortable with hypothesis testing, probability distributions, and algorithmic bias mitigation.
- Apply Bayesian techniques for uncertainty estimation.
- Use ensemble methods to improve predictive power.
- Monitor model drift over time.
Domain Knowledge in Finance
Experience in credit scoring, fraud detection, or customer segmentation is highly valued.
Knowledge of regulatory frameworks such as GLBA or GDPR will help you navigate compliance challenges.
- Understand loan underwriting processes.
- Interpret financial statements for data context.
- Apply risk‑adjusted performance metrics.
Desired Qualifications
Beyond the core requirements, we look for candidates who bring creativity and leadership to the table.
Strong communication skills and a collaborative mindset will help you thrive in a hybrid environment.
- Master’s or PhD in Statistics, Computer Science, or related field.
- Published research or contributions to open‑source projects.
- Experience leading data science teams.
Leadership & Mentorship
Guiding junior data scientists and fostering a culture of learning is part of the role.
You’ll provide code reviews, share best practices, and drive knowledge transfer.
- Conduct training sessions on new tools.
- Mentor peers on model validation techniques.
- Encourage experimentation and iterative improvement.
Communication Excellence
Being able to translate technical findings into plain language is key.
Stakeholders rely on your insights to shape strategy.
- Prepare concise executive summaries.
- Present findings in virtual meetings.
- Document assumptions and limitations clearly.
Compensation & Benefits
While salary is negotiable, GM Financial offers a comprehensive benefits package that includes health coverage, retirement plans, and professional development funds.
Additional perks such as flexible work hours, wellness programs, and tech allowances support your overall well‑being.
- Health, dental, and vision insurance.
- 401(k) with company match.
- Paid time off and remote work stipend.
- Continuous learning budget.
How to Apply
Ready to take the next step? Gather your resume, portfolio of projects, and a cover letter that highlights your impact in data science.
Submit your application through the official site or reach out to the HR team directly. Make sure to tailor your materials to reflect the responsibilities and skills outlined above.
- Prepare a concise résumé with quantifiable achievements.
- Include a portfolio link to code repositories or Kaggle notebooks.
- Write a cover letter that showcases your passion for finance and data.
Application Checklist
Before hitting submit, double‑check that you meet the core criteria and that your documents are polished.
- Resume updated to reflect recent projects.
- Cover letter tailored to GM Financial’s mission.
- Portfolio or GitHub link verified.
- References ready if requested.
Frequently Asked Questions
Below are common queries that may help you prepare for the hiring process.
- What does a hybrid remote schedule look like? You’ll work from home most days and attend in‑office sessions a few times a month.
- What tools will I use? Python, SQL, cloud services, and collaboration platforms.
- Is relocation supported? Depending on location, relocation packages may be available.
Conclusion
If you’re a seasoned data scientist eager to shape financial solutions in a hybrid environment, this opportunity at GM Financial could be your next career milestone. Bring your expertise, curiosity, and collaborative spirit to a role that rewards innovation and impact.
Apply now if you meet the qualifications and are ready to contribute to a team that values data‑driven decision making. Your next challenge awaits.
