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About This Role
Overview
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At Ford, you’ll work on ideas that matter, alongside passionate people who want to make a global impact. Together, we’re shaping the next era of transportation—grounded in purpose, driven by progress. Make your move.
- Job Type: Full time
- Work Type: Remote
We made history and now we work to transform the future – for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.
The Ford Motor Credit Company team helps put people behind the wheels of great Ford and Lincoln vehicles. By partnering with dealerships, we provide financing, personalized service and professional expertise to thousands of dealers and millions of customers in over one hundred countries around the world.
In this position...
The Senior Data Scientist on the Credit AI team at Ford Credit will lead the development and deployment of advanced AI and machine learning solutions that improve customer experience, reduce risk, and drive operational efficiency. This role focuses on delivering scalable, production\-ready solutions across conversational AI, fraud detection, forecasting, and intelligent automation initiatives while partnering closely with engineering, product, and business stakeholders.
As a Senior Data Scientist within the Credit AI organization, you will play a critical role in shaping and delivering AI\-driven solutions that support strategic business priorities across Ford Credit. You will work across a diverse portfolio of initiatives, including conversational AI solutions for customer representatives, fraud detection and risk analytics, forecasting and predictive modeling, and AI agents that automate business workflows and accelerate software development processes.
This role requires strong expertise in machine learning, statistical modeling, generative AI, and production AI systems. You will collaborate with cross\-functional teams to translate business challenges into scalable technical solutions, develop and validate models, and ensure successful deployment into production environments. You will also help establish best practices around model governance, monitoring, explainability, and responsible AI.
The ideal candidate combines deep analytical and technical expertise with strong business acumen, communication skills, and the ability to lead complex initiatives from concept through implementation. Success in this role will be measured through measurable business outcomes such as reduced fraud losses, improved forecast accuracy, enhanced customer support efficiency, and increased automation effectiveness.
What you'll do...
- Design, develop, validate, and deploy machine learning and AI solutions for business\-critical applications.
- Build scalable predictive models, anomaly detection systems, forecasting solutions, recommendation systems, and generative AI applications.
- Develop conversational AI and agent\-assist solutions leveraging LLMs, NLP, and retrieval\-augmented generation (RAG) techniques.
- Create intelligent AI agents for business workflow automation and SDLC acceleration initiatives.
- Develop and optimize fraud detection models using supervised and unsupervised machine learning techniques.
- Analyze structured and unstructured datasets to identify trends, patterns, risks, and business opportunities.
- Partner with engineering teams to productionize AI/ML solutions and integrate them into enterprise applications and workflows.
- Develop reusable ML pipelines, feature engineering frameworks, and model monitoring capabilities.
- Monitor model performance, drift, reliability, and operational effectiveness in production environments.
- Collaborate with product managers, engineers, business stakeholders, and risk/compliance teams to define requirements, success metrics, and implementation strategies.
- Translate technical insights and analytical findings into clear business recommendations and executive\-level communications.
- Ensure AI and machine learning solutions comply with data governance, privacy, security, and regulatory standards.
- Develop documentation supporting model explainability, validation, monitoring, and audit readiness.
- Promote responsible AI practices, including fairness, transparency, and risk mitigation.
- Mentor junior team members and contribute to technical standards, best practices, and continuous improvement initiatives.
You'll have...
Required Qualifications
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- 5\+ years of experience developing and deploying machine learning or AI solutions in production environments.
- Strong programming experience in Python and experience with ML frameworks such as scikit\-learn, PyTorch, TensorFlow, or similar.
- Experience building predictive models, forecasting solutions, anomaly detection systems, NLP applications, or generative AI solutions.
- Experience with large language models (LLMs), prompt engineering, retrieval\-augmented generation (RAG), or conversational AI systems.
- Strong SQL and data manipulation skills with experience working on large\-scale datasets.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Understanding of MLOps concepts including model deployment, monitoring, versioning, and CI/CD workflows.
- Strong analytical, problem\-solving, communication, and stakeholder management skills.
Even better, you may have...
- Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- Experience in financial services, credit risk, fraud analytics, or regulated industries.
- Experience with AI agents, orchestration frameworks, or automation platforms.
- Experience with model explainability and governance tools such as SHAP or LIME.
- Knowledge of software engineering workflows and developer productivity tooling.
- Experience mentoring or leading technical teams.
You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!
As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder…or all of the above? No matter what you choose, we offer a work life that works for you, including:
- Immediate medical, dental, vision and prescription drug coverage
- Flexible family care days, paid parental leave, new parent ramp\-up programs, subsidized back\-up child care and more
- Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
- Vehicle discount program for employees and family members and management leases
- Tuition assistance
- Established and active employee resource groups
- Paid time off for individual and team community service
- A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
- Paid time off and the option to purchase additional vacation time.
This position is a range of salary grade 7 that ranges from $99,600\-$166,600 to salary grade 8 and that ranges from $115,000\-$192,900\.
Final determination of salary grade will be based on candidate's skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.
For more information on salary and benefits, click here: https://fordcareers.co/GSR
Visa sponsorship is not available for this position.
Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1\-888\-336\-0660\.
\#LI\-Remote\#LI\-FordCredit \#LI\-MN1
Salary Context
This $99K-$192K range is below the median for Data Scientist roles in our dataset (median: $162K across 211 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Ford Motor Company, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $200,000 based on 697 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($146K) sits 27% below the category median. Disclosed range: $99K to $192K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Ford Motor Company AI Hiring
Ford Motor Company has 4 open AI roles right now. They're hiring across Data Scientist, AI Software Engineer, AI/ML Engineer. Positions span Remote, US, Dearborn, MI, US. Compensation range: $192K - $250K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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