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About This Role
This is a technical individual contributor role that designs, builds, and operates the demand\-side modeling and simulation systems for Rivian’s remarketing business. The role develops production forecasting models for order volume, market sizing, price sensitivity, and customer segmentation, implemented in Python and Databricks within Git\-versioned repositories with code review, automated testing, and CI/CD. Model outputs drive sales strategy, product positioning, and demand planning for the Pre\-Owned business.
The Sr. Data Science, Demand Decision Systems role combines applied data science, analytics engineering, and product ownership: the role both engineers the forecasting systems and is accountable for the quality of the demand decisions they inform. Success is measured by the technical robustness of the systems built and the integrity of the plans they produce.* Design, build, and operate production forecasting and simulation systems. Develop Python\-based simulation and forecasting models in Databricks as a member of a highly technical team designing interconnected models. Work in Git\-versioned repositories with merge\-request review, automated testing, and CI/CD pipelines (GitLab), and apply AI\-assisted and agentic development workflows as a standard part of the engineering stack.
- Statistical and machine learning model development. Design, validate, and maintain the models that drive demand decisions: time\-series order volume forecasting, price sensitivity and willingness\-to\-pay estimation, customer segmentation, and demand\-sensing models. Apply statistical, machine learning, and deep learning methods where appropriate, with backtesting and production performance monitoring.
- Demand data products and pipelines. Build and maintain the data models and pipelines that describe demand, covering sales transactions, vehicle configuration, geographic and pricing signals, and customer attributes, with data contracts, tests, and documentation that allow downstream decision systems and planning tools to consume them reliably.
- AI\-augmented engineering. Apply AI\-assisted and agentic development workflows as a first\-class part of the engineering stack. Evaluate and integrate AI tooling into production engineering workflows and set the patterns the team follows.
- Market sizing and demand\-sensing models. Build the models that size the market for used Rivians and competitive vehicles, project market and environment changes, and quantify where and how demand is shifting, including how and where competitors sell used Rivians. Maintain authoritative, versioned market\-size and demand baselines consumed by long\-range planning.
- Supply\-demand balancing. Model the interaction between incoming vehicle supply and demand levers to engineer healthy future sales volumes. Work with supply\-focused colleagues to integrate supply constraints, and quantify the risk, cost, and expected impact of proposed demand\-generation actions through scenario simulation.
- Operationalize model outputs with business partners. Translate model outputs into pricing, positioning, and sales\-strategy actions with marketing and sales partners, and align with the team on decision\-system and long\-range planning tool design.
- Proficiency with Python, SQL, and Databricks (or equivalent warehouse/lakehouse platform); experience with dbt or equivalent transformation frameworks.
- Experience with Git\-based engineering workflows, code review, and CI/CD pipelines (GitLab or equivalent).
- Demonstrated experience building and operating production forecasting or demand models end\-to\-end, including data modeling, pipeline orchestration, model validation, testing, and deployment.
- Demonstrated ability to design and validate applied statistical and machine learning models, including time\-series forecasting, demand modeling, segmentation, or elasticity estimation.
- Experience in a technical demand\-forecasting, marketing science, or sales\-forecasting role (such as data science on a marketing team or quantitative forecasting at a sales\-driven company).
- Demonstrated ability to translate ambiguous business questions into production data products and durable models.
Preferred Qualifications
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- Bachelor’s degree or higher in a quantitative or technical field (Computer Science, Data Science, Statistics, Mathematics, Industrial Engineering, or similar).
- Experience applying deep learning methods to forecasting, segmentation, or demand\-sensing problems.
- Experience integrating external APIs and third\-party data sources into production data systems.
- Experience with AI\-assisted development workflows, agentic coding tools, and integrating AI tooling into production engineering processes.
- Experience in automotive, marketplace, e\-commerce, or adjacent customer\-facing domains.
- Familiarity with BI and analytics tools such as Hex, Looker, or equivalent.
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 Rivian, 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.
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.
Rivian AI Hiring
Rivian has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Palo Alto, CA, US.
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.
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