Data Scientist

MV Transportation•Dallas, TX

About The Position

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. 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, and engage Finance and Procurement on cost, ROI, and optimization tradeoffs. You will also 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.

Requirements

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.
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, or Agentic AI is appropriate.
Commitment to responsible AI deployment, particularly in safety-, equity-, and compliance-sensitive transportation systems.

Nice To Haves

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.

Responsibilities

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, and service reliability, on-time performance, and cost optimization.
Balance mathematical optimality with operational practicality and change management.
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, and human-in-the-loop controls for safety-critical decisions.
Establish shared standards for governance, observability, safety, and accountability of agentic AI across departments.
Partner with data engineering and platform teams to deliver solutions on Microsoft Fabric, including OneLake, Lakehouses, and Warehouses, Fabric Notebooks (Python / Spark), and 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.
Bring PhD-level rigor into applied, cross-functional problem solving.
Translate advances in operations research, machine learning, reinforcement learning, and agentic and autonomous systems into solutions that can be operationalized and sustained.
Produce internal frameworks, playbooks, and reference architectures used across teams.
Serve as a trusted advisor to senior leaders on AI investment decisions, automation risk and readiness, and 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.