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About This Role
Here at Scout Motors, we're carrying forward the heritage of one of the most iconic American vehicles in history. A vehicle dating back to 1960\. One that forged the path for future generations of rugged SUVs and trucks and will do so once again.
But Scout is more than just a brand, it's a legacy steeped in a culture of exploration, caretaking, and hard work.
The Scout brand is all about respect. Respect for the past and the future by taking an iconic American brand that hasn't been around for a while, electrifying it, digitizing it, and loading it with American innovation. Respect for communities by creating a company that stands for its people and its customers. Respect for both work and play, with vehicles that are equally at home at a camp site, a job site, or on a Tuesday commute. And respect for our customers by developing two powertrains that meet their requirements — an all\-electric powertrain as well as the Harvester™ range extender powertrain which includes a built\-in gas\-powered generator with an estimated 500 miles of combined range.
At Scout Motors, we empower our talented, inclusive, and entrepreneurial teams to innovate. What makes a Scout employee? Someone who is a visionary and a leader, who seeks new paths and shares lessons learned. A knowledgeable doer who collaborates across the company to build better. A go\-getter with unrivaled passion.
Join us at Scout Motors and be part of shaping the future of transportation. If you're ready to drive change and make history, apply now!
#### Role Overview
Scout Motors is building its first enterprise demand forecast \- and there's no sales history to build it on.
As a VW Group–backed EV company in the run\-up to launch, Scout has reservations data, an enthusiastic and loyal community, and a product about to enter the market — but not the years of historical sales a demand model usually leans on. That's the problem this role owns: building a rigorous, probabilistic forecast from the signals that *do* exist, and growing it into the analytical engine of Scout's planning system as real demand data arrives.
This is the first dedicated modeling seat in Integrated Business Planning. You'll develop the demand forecasting model and own the data behind it — then partner with IT and Product Management to productionize it into the digital IBP product Scout is building, so the forecast lives where the business actually plans and decides.
#### What you'll do
Become part of an iconic brand that is set to revolutionize the electric pick\-up truck \& rugged SUV marketplace by achieving the following:
- Build Scout's first demand forecasting model — probabilistic rather than point\-estimate, forecasting *unconstrained* demand at the grain the business plans on. Convert reservation backlog with explicit probabilities, phase the timing of that conversion, and layer in organic demand as its own signal.
- Solve the cold\-start problem — stand up a credible forecast *before* there's sales history, using reservations, configurator and order data, market analogs, and structured priors — then systematically replace assumptions with data as the market gives it to us.
- Own the demand data end to end — source, define, and steward the data that feeds the forecast; build the pipelines and the feature/data layer; own data quality and the shared definitions everyone downstream depends on.
- Partner across the business to get the inputs and outputs right — work with the teams that own the signals feeding the forecast and the teams that consume it, so the model is grounded in real inputs and lands in a format the business can actually use.
- Make the forecast legible — document methodology and assumptions, quantify uncertainty honestly, and explain the model's logic clearly to non\-technical stakeholders in Finance, Commercial, Production, and Procurement.
- Productionize with IT and Product — partner with engineering to move the model from notebooks into the IBP digital product (pipelines, deployment, monitoring, retraining)
- Stand up forecast measurement — back\-testing, accuracy tracking, and model monitoring, so the forecast improves on a known cadence as post\-launch data accumulates and the model is retrained against reality.
- Lay the foundation for what's next — build the data and modeling base that Scout's future AI/ML planning capabilities will extend.
Location \& Travel Expectations:
- This role may be based out of the Scout Motors corporate headquarters in Charlotte, NC.
- This role requires 4\-5 days per week in the office, with regular in\-person meetings and events.
- Applicants should expect that the role will require the ability to convene with Scout colleagues in person and travel to participate in events on behalf of the company from time to time.
#### What you'll bring
We expect all Scout employees to have integrity, curiosity, resourcefulness, and strive to exhibit a positive attitude, as well as a growth mindset. You'll be comfortable with change and flexible in a fast\-paced, high\-growth environment. You'll take a collaborative approach to achieve ambitious goals. Here's what else you'll bring:
- 12\+ years of applied data science or forecasting, or equivalent experience — you've built and shipped statistical or ML models that other people depend on, not just one\-off analyses.
- MS of PhD in a quantitative discipline (statistics, applied math, physics, operations research, economics, or similar), or equivalent applied experience
- A strong probabilistic and time\-series modeling foundation — hierarchical, Bayesian, or other methods suited to granular, sparse, and uncertain demand. You reason in distributions, not just predictions.
- Fluent in Python and the modern modeling stack, strong SQL, and comfortable owning data end to end — pipelines, quality, and definitions, not just the model that sits on top.
- Experience forecasting with sparse, new\-product, or cold\-start data — analogs, priors, judgment\-augmented methods — and honest about the limits of each.
- You've put models into production alongside engineering and product; you think about deployment, monitoring, and retraining, not just the notebook.
- You work fluently with AI coding tools (e.g., Claude Code, Copilot, Cursor) to build, prototype, and ship faster — and have the judgment to know where they help and where they don't.
- You translate technical work for non\-technical audiences and can defend a forecast to a skeptical, senior, cross\-functional room.
- A high\-ownership mindset, comfortable in a fast\-moving, build\-it\-yourself environment where the data and the priorities are still maturing
- Nice to have: automotive, manufacturing, or other physical\-product demand; demand sensing or hierarchical forecasting; ML Ops; experience embedding models inside a digital product.
#### What you'll gain
The benefits of joining Scout include the chance to build products and a company from the ground up. This is a chance to create something new and lasting – with an iconic brand at its foundation. In addition, Scout provides competitive compensation and benefits to support your physical, mental, and financial wellbeing. Program specifics are detailed in company policies and employee benefit guides, select highlights:
- Competitive insurance including:
+ Medical, dental, vision and income protection plans
- 401(k) program with:
+ An employer match and immediate vesting
- Generous Paid Time Off including:
+ 20 days planned PTO, as accrued
+ 40 hours of unplanned PTO and 14 company or floating holidays, annually
+ Up to 16 weeks of paid parental leave for biological and adoptive parents of all genders
+ Paid leave for circumstances related to bereavement, jury duty, voting time, or military leave
#### Why This Role Matters
Scout's entire planning system reconciles demand against supply and this is the seat that builds the demand side of that equation. You'll own the model and the data from version one, with the rare chance to design them right before launch rather than retrofit them under pressure after. As Integrated Business Planning scales and the planning product matures, the person who built the forecast is positioned to grow into deeper technical and team ownership.
#### Pay Transparency
This is a full\-time, exempt position eligible to receive a base salary and to participate in an annual performance bonus program. Final salary offered will be determined based on factors including but not limited to the candidate's skills and experience. The annual performance bonus program is preset and not candidate dependent.
Initial base salary range \= $160,000\.00 \- $192,500\.00
Internal leveling code: IC7
Notice to applicants:
- To be considered for career opportunities at Scout Motors, applicants must be 18 years of age or older.
- Residing in San Francisco: *Pursuant to the San Francisco Fair Chance Ordinance, Scout Motors will consider for employment qualified applicants with arrest and conviction records.*
- Residing in Los Angeles: *Scout Motors will consider for employment qualified applicants with criminal histories in a manner consistent with the Los Angeles Fair Chance Initiative for Hiring Ordinance.*
- Residing in New York City: *This role is not eligible for remote work in New York City.*
Equal Opportunity
Scout Motors is committed to employing a diverse workforce and is proud to be an Equal Opportunity Employer. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, disability, pregnancy, or any other characteristics protected by law. Scout Motors is committed to compliance with all applicable fair employment practice laws. If you require reasonable accommodation to complete a job application, pre\-employment testing, or a job interview or to otherwise participate in the hiring process, please contact [email protected].
Salary Context
This $160K-$192K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Scout Motors, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($176K) sits 9% below the category median. Disclosed range: $160K to $192K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Scout Motors AI Hiring
Scout Motors has 1 open AI role right now. They're hiring across Data Scientist. Based in Charlotte, NC, US. Compensation range: $192K - $192K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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