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About This Role
Overview:
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Responsibilities:
We are seeking a Data Scientist with strong industry experience to operate as a cross‑functional leader at the intersection of transportation operations, operations research, AI, and business strategy. This role partners deeply with Operations, Technology, Product, Finance, and Executive Leadership to design and deploy analytics and AI solutions that materially improve service reliability, efficiency, and decision‑making.
In addition to advanced modeling, this role serves as the enterprise champion for Agentic AI adoption, ensuring autonomous and semi‑autonomous systems are introduced responsibly, collaboratively, and with measurable operational impact. Cross‑Functional Operating Model
This role does not sit in isolation within data science. Instead, you will:* Co‑create solutions with Operations and Planning leaders
- Partner with IT, Data Engineering, and Platform teams on scalable cloud architectures
- Collaborate with Product and Innovation teams on user‑centric decision tools
- Engage Finance and Procurement on cost, ROI, and optimization tradeoffs
- Advise Executive Leadership on AI strategy, automation risk, and operational impact
You are expected to influence without authority and act as a unifying technical and strategic voice across functions. Key ResponsibilitiesTransportation Operations \& OR (Business‑Embedded)* Work directly with operations, dispatch, and planning teams to understand constraints, tradeoffs, and real‑world decision processes.
- Design and deploy operations research and analytics solutions for:
- + Scheduling and rostering
+ Fleet sizing and allocation
+ Demand forecasting and capacity planning
+ Service reliability, on‑time performance, and cost optimization
- Balance mathematical optimality with operational practicality and change management.
Agentic AI Champion (Enterprise‑Wide)* Act as the cross‑functional champion for Agentic AI, driving alignment across technical, operational, and leadership teams.
- Identify opportunities where agent‑based systems can augment planners, dispatchers, analysts, and executives.
- Design agentic workflows that integrate:
- + Planning and reasoning
+ Optimization tools and simulation engines
+ Data platforms, APIs, and business rules
+ Human‑in‑the‑loop controls for safety‑critical decisions
- Establish shared standards for governance, observability, safety, and accountability of agentic AI across departments.
Microsoft Fabric \& Cloud‑First Enablement* Partner with data engineering and platform teams to deliver solutions on Microsoft Fabric, including:
- + OneLake, Lakehouses, and Warehouses
+ Fabric Notebooks (Python / Spark)
+ Power BI semantic models for operational decision support
- Ensure analytics and AI outputs are consumable by both technical and non‑technical users.
- Influence cloud architecture decisions to support real‑time and large‑scale transportation analytics.
Research‑to‑Operations Translation* Bring PhD‑level rigor into applied, cross‑functional problem solving.
- Translate advances in:
- + Operations research
+ Machine learning
+ Reinforcement learning
+ Agentic and autonomous systems
into solutions that can be operationalized and sustained.
- Produce internal frameworks, playbooks, and reference architectures used across teams.
Strategic Influence \& Enablement* Serve as a trusted advisor to senior leaders on:
- + AI investment decisions
+ Automation risk and readiness
+ Tradeoffs between cost, service quality, and equity
- Mentor data scientists, analysts, engineers, and operations staff to raise AI literacy across the organization.
- Facilitate cross‑functional forums or working groups around analytics, AI, and automation.
Qualifications:
Required Qualifications* Masters/PhD in Operations Research, Industrial Engineering, Transportation Engineering, Computer Science, Applied Mathematics, Statistics, or a related field.
- 7\+ years of industry experience working in transportation, logistics, mobility, or complex operational environments.
- Demonstrated success operating in highly cross‑functional settings.
- Deep expertise in optimization, simulation, and statistical modeling, combined with ML.
- Strong programming skills in Python; experience integrating OR solvers and dashboards.
- Experience delivering solutions in cloud‑based, enterprise environments.
- Exceptional communication and stakeholder‑management skills.
Agentic AI–Specific Requirements* Experience leading or designing agentic AI systems across multiple teams or functions.
- Ability to explain agentic concepts clearly to operations, leadership, IT, and risk teams.
- Strong judgment in distinguishing when:
- + Deterministic OR is sufficient
+ ML adds value
+ Agentic AI is appropriate
- Commitment to responsible AI deployment, particularly in safety‑, equity‑, and compliance‑sensitive transportation systems.
Preferred Qualifications* Experience in public transit, paratransit, logistics, or large fleet operations.
- Familiarity with Microsoft Azure and Fabric‑based analytics ecosystems.
- Experience influencing AI governance, operating models, or centers of excellence.
- Prior leadership in enterprise transformation or modernization initiatives.
What Success Looks Like* Operations trust and actively use analytics and AI solutions.
- Agentic AI is adopted intentionally, safely, and cross‑functionally—not in silos.
- Microsoft Fabric enables shared, consistent decision‑making across teams.
- Leadership views this role as a connector between strategy, technology, and day‑to‑day operations.
*MV Transportation is committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin or ancestry, sex, physical or mental disability, veteran or military status, genetic information or any other legally recognized protected basis under federal, state or local laws, regulations or ordinances. The information collected by this application is solely to determine suitability for employment, verify identity and maintain employment statistics on applicants.* *Where permissible under applicable state and local law, applicants may be subject to a pre\-employment drug test and background check after receiving a conditional offer of employment.* *\#appcast*
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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At MV Transportation, 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
MV Transportation AI Hiring
MV Transportation has 1 open AI role right now. They're hiring across Data Scientist. Based in Dallas, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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