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About This Role
remote type
Hybrid
locations
Fort Mill, SC US
time type
Full time
posted on
Posted Today
time left to apply
End Date: August 4, 2026 (13 days left to apply)
job requisition id
DT\-18922Inside the Role
The Pricing Strategy Analyst will support the Pricing Strategy team by analyzing pricing, financial, and competitive data to help identify opportunities that drive profitable growth for DTNA Parts. The role will focus on building analytical insights, supporting pricing initiatives, and enabling data\-driven decision\-making in close collaboration with cross\-functional partners.
Example activities include analyzing pricing across products and customer segments, supporting the development and enhancement of pricing logic, and building reports or dashboards to track pricing performance and outcomes.
The Pricing Strategy Analyst is expected to work closely with Pricing, Marketing, Sales, Product, Finance, and Analytics teams, translating complex data into clear insights and actionable recommendations. The role requires a strong willingness to learn DTNA’s products, markets, and pricing frameworks, while progressively taking on more complex analytical and strategic responsibilities over time.Posting Information
We provide a scheduled posting end date to assist our candidates with their application planning. While this date reflects our latest plans, it is subject to change, and postings may be extended or removed earlier than expected.
We Take Care of Our Team
Position offers a starting salary range of $86,000 \- $110,000 USD
Pay offered dependent on knowledge, skills, and experience
Benefits include annual bonus program; 401k company contribution with company match up to 6% as well as non\-elective company contribution of 3 \- 7% depending on age; starting at 4 weeks paid vacation; 13\+ calendar holidays; 8 weeks paid parental leave; employee assistance program; comprehensive healthcare plans and wellness programs; onsite fitness (at some locations); tuition assistance and volunteer paid time off; short\-term and long\-term disability plans.
What You Drive at DTNA
- Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex high\-volume data from a variety of sources
- Develops and maintains infrastructure systems that connect internal data sets; creates new data collection frameworks for structured and unstructured data
- Analyze pricing, sales, cost, and competitive data to support strategic pricing initiatives.
- Build and maintain analytical models to evaluate competitive position, pricing scenarios, and performance trends
- Develop, maintain and enhance dashboards and reports to track pricing KPIs and communicate insights to stakeholders
- Partner with Pricing, Marketing Sales, Product, Finance, and Analytics teams on cross\-functional initiatives
- Support ad\-hoc analyses and deep dives to answer time\-sensitive business questions
- Contribute to the continuous improvement of pricing tools, processes, and data quality
Knowledge You Should Bring
- Bachelor's degree in Data Science, Economics, Engineering, Statistics, Business, Computer Science, Artificial Intelligence, or a related field.
- Strong analytical and problem\-solving skills with attention to detail.
- Proficiency in Python and SQL for data analysis, statistical modeling, and machine learning development.
- Experience applying predictive analytics, machine learning, optimization techniques, or advanced statistical methods to solve business problems.
- Experience handling large datasets; familiarity with visualization tools (e.g., Tableau, Power BI) is preferred.
- Experience with Python data science libraries such as Pandas, NumPy, Scikit\-Learn, TensorFlow, PyTorch, or similar technologies is preferred.
- Strong communication skills and ability to work collaboratively across teams.
- Eagerness to learn, take feedback, and grow in a fast\-paced environment.
- Contribute to the continuous improvement of pricing tools, processes, and data quality.
Exceptional Candidates Might Have
- Experience with Generative AI, Large Language Models (LLMs), prompt engineering, or AI\-assisted analytics solutions.
- Experience developing and deploying machine learning models in Azure, AWS, or Google Cloud environments.
- Experience with advanced analytics techniques such as forecasting, optimization, price elasticity modeling, recommendation systems, or causal inference.
- Master's degree in Data Science, Artificial Intelligence, Statistics, Operations Research, Computer Science, or a related quantitative field.
- The ideal candidate is a highly motivated early\-career analyst who enjoys working with data and solving business problems. They are collaborative, intellectually curious, and excited to build a foundation in pricing strategy within a leading OEM organization.
\#LI\-TN1 \#LI\-Hybrid
Where We Work
This position is open to applicants who can work in (or relocate to) the following location(s)\-
Fort Mill, SC US. Relocation assistance is not available for this position.Schedule Type:
Hybrid (4 days per week in\-office / 1 day remote). This schedule builds our \#OneTeamBestTeam culture, provides an unparalleled customer experience, and creates innovative solutions through in\-person collaboration.
At Daimler Truck North America, we recognize our world is changing faster than ever before. By listening to the needs of today, we’re building to solve with cutting\-edge solutions in sustainability and future driving technology across electric, hydrogen and autonomous. These solutions, backed by years of innovative success and achievement, continue DTNA’s legacy as the undisputed industry leader. Our evolving brand portfolio is second to none, including Freightliner Trucks, Western Star, Demand Detroit, Thomas Built Buses, Freightliner Custom Chassis, and Financial Services. Together, we work as one team towards our envisioned future – building a cleaner, safer and more efficient tomorrow for all.
That is what we are working toward \- for all who keep the world moving.
Additional Information
- This position is not open for Visa sponsorship or to existing Visa holders
- Applicants must be legally authorized to work permanently in the country the position is located in at the time of application
- Final candidate must successfully complete a criminal background check
- Final candidate may be required to successfully complete a pre\-employment drug screen
- Contractors, professional services, or other contingent workers should confirm with their local agency if they are eligible to apply for FTE positions
- EEO \- Disabled/Veterans
Daimler Truck North America is committed to workforce inclusion and providing an environment where equal employment opportunities are available to all applicants and employees without regard to race, color, sex (including pregnancy), religion, national origin, age, marital status, family relationship, disability, sexual orientation, gender identity and expression (including transgender and transitioning status), genetic information, or veteran status.
For an accommodation or special assistance with applying for a posted position, please contact our Human Resources department at 503\-745\-8982 or toll free 800\-206\-3369\. For TTY/TDD enabled call 503\-745\-2137 or toll free 866\-355\-6935\.
Salary Context
This $86K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Daimler Truck North America, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($98K) sits 55% below the category median. Disclosed range: $86K to $110K.
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.
Daimler Truck North America AI Hiring
Daimler Truck North America has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Mill, SC, US. Compensation range: $110K - $110K.
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 AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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