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About This Role
Job Description
Mission
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Turn complex business questions and high\-value data into trustworthy, production\-grade machine\-learning solutions that improve decisions, automate work, and create measurable business impact across Sales, Service, Marketing, and Global Markets.
This is a hands\-on Staff Data Scientist role for an experienced individual contributor who can move seamlessly from business problem framing and analytical discovery to feature engineering, model development, production deployment, and continuous improvement. The role combines deep technical expertise with strong business judgment, helping teams adopt rigorous, interpretable, and reusable data\-science practices at scale.
Key Responsibilities
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### Applied Machine Learning
Translate ambiguous business problems into clear analytical objectives, modeling strategies, and measurable success criteria.
- Develop, validate, and improve predictive, prescriptive, forecasting, optimization, classification, and segmentation models.
- Select appropriate statistical and machine\-learning techniques based on the business decision, available data, operational constraints, and expected value.
- Apply advanced methods such as time\-series forecasting, causal inference, experimentation, natural\-language processing, and optimization when they are fit for purpose.
### Data and Feature Engineering
- Define data requirements and partner with data engineering and business teams to establish reliable, well\-documented data sources.
- Build scalable, reproducible feature pipelines and reusable analytical assets.
- Perform exploratory analysis, data\-quality assessment, feature selection, and leakage detection to ensure models are based on sound data.
- Work across structured and unstructured data, including customer, vehicle, dealer, sales, service, warranty, incentive, and operational datasets.
### Model Evaluation and Decision Quality
- Establish rigorous evaluation frameworks that reflect real\-world business outcomes, not only offline technical metrics.
- Assess model performance, calibration, bias, interpretability, robustness, and operational fit.
- Explain model behavior, assumptions, limitations, and recommendations clearly to technical and nontechnical stakeholders.
- Design and analyze experiments, pilots, and champion/challenger approaches to validate value before broad adoption.
### Production ML and MLOps
- Package and deploy models as reliable production services, batch processes, or decision\-support capabilities in partnership with software, data, and platform engineers.
- Establish reproducible practices for dependency management, versioning, data lineage, experiment tracking, and model release management.
- Design model monitoring for accuracy, data quality, drift, latency, availability, and business performance.
- Define practical drift thresholds, automated alerts, retraining criteria, and service\-level expectations for models operating in production.
- Investigate production issues, identify root causes, and improve models and pipelines through structured iteration.
### Business Partnership and Delivery
- Collaborate with product leaders, business owners, architects, engineers, IT, Finance, and other partners to deliver end\-to\-end solutions.
- Connect technical work to measurable outcomes such as revenue growth, cost reduction, productivity, customer experience, risk reduction, or improved operational decisions.
- Balance analytical sophistication with usability, speed to value, maintainability, and adoption.
- Lead the data\-science workstream from concept through production and continuous improvement, maintaining clear documentation and delivery accountability.
### Technical Leadership and Enablement
- Serve as a technical authority and trusted advisor on machine learning, statistical modeling, experimentation, and production data science.
- Raise the quality bar for model development through reusable patterns, code reviews, documentation, testing, and reproducibility.
- Coach data scientists, analysts, engineers, and citizen builders on sound modeling practices and responsible use of AI.
- Help teams evaluate and use platforms such as Databricks, Azure AI, Glean, and other enterprise tooling when they accelerate delivery without compromising quality.
- Share lessons learned, reusable components, and practical guidance across the AI Center and partner organizations.
Required Qualifications
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- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field; advanced degree preferred.
- 8\+ years of professional experience in data science, machine learning, applied statistics, or a closely related discipline.
- Demonstrated experience taking machine\-learning solutions from problem definition and proof of concept through production deployment and ongoing operation.
- Strong proficiency in Python and SQL, including experience with production\-quality code, testing, version control, and documentation.
- Strong hands\-on experience with common data\-science and machine\-learning libraries such as Pandas, NumPy, scikit\-learn, PyTorch, TensorFlow, or equivalent technologies.
- Experience with feature engineering, model evaluation, experiment design, statistical analysis, and communicating results to nontechnical audiences.
- Experience deploying models through APIs, batch pipelines, notebooks\-to\-production workflows, or comparable production patterns.
- Practical understanding of MLOps, including experiment tracking, model versioning, data and model monitoring, drift detection, retraining, and release management.
- Experience working with large\-scale data platforms such as Databricks, Spark/PySpark, cloud data warehouses, or equivalent technologies.
- Demonstrated ability to operate independently, make sound technical tradeoffs, and deliver in a fast\-changing, cross\-functional environment.
Preferred Qualifications
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- Master’s or PhD in Statistics, Computer Science, Machine Learning, Operations Research, Mathematics, or a related quantitative field.
- Experience in automotive, sales, service, marketing, customer analytics, dealer analytics, warranty, incentives, forecasting, or other operationally complex domains.
- Experience with causal inference, time\-series forecasting, optimization, recommendation systems, natural\-language processing, or generative\-AI\-enabled analytical workflows.
- Experience with MLflow or comparable tools for experiment tracking, model registry, and lifecycle management.
- Experience with Azure, Databricks, REST APIs, containerized deployment, CI/CD, and cloud\-native data or ML services.
- Experience defining model governance, responsible\-AI controls, interpretability practices, or risk\-based evaluation standards.
- Experience quantifying financial impact and partnering with Finance or business leaders to validate value realization.
- Familiarity with enterprise AI platforms, including Glean, Azure AI Foundry, Databricks, or comparable platforms.
Compensation:
The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws.
The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate.
- The salary range for this role is $160,000\-$246,000 . The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
- Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
- Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation \& holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more
\#LI\-HP2
\&\#xa;\&\#xa;GM does not provide immigration\-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1\-B, OPT, STEM OPT, CPT, TN, J\-1, etc).\&\#xa;\&\#xa;This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}.\&\#xa;\&\#xa;This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.\&\#xa;\&\#xa;
About GM
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
Why Join Us
We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Benefits Overview
From day one, we're looking out for your well\-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources .
Non\-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
All employment decisions are made on a non\-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role\-related assessment(s) and/or a pre\-employment screening prior to beginning employment. To learn more, visit How we Hire .
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1\-800\-865\-7580\. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Salary Context
This $160K-$246K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At General Motors (GM), 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($203K) sits 5% above the category median. Disclosed range: $160K to $246K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Motors (GM) AI Hiring
General Motors (GM) has 13 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Warren, MI, US, Austin, TX, US, Sunnyvale, CA, US. Compensation range: $173K - $335K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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