Data Scientist

Role overview

Responsibilities

• check_circle Co‑create solutions with Operations and Planning leaders
• check_circle Partner with IT, Data Engineering, and Platform teams on scalable cloud architectures
• check_circle Collaborate with Product and Innovation teams on user‑centric decision tools
• check_circle Engage Finance and Procurement on cost, ROI, and optimization tradeoffs
• check_circle Advise Executive Leadership on AI strategy, automation risk, and operational impact
• check_circle Work directly with operations, dispatch, and planning teams to understand constraints, tradeoffs, and real‑world decision processes.
• check_circle Design and deploy operations research and analytics solutions for:
• check_circle + Scheduling and rostering Fleet sizing and allocation Demand forecasting and capacity planning Service reliability, on‑time performance, and cost optimization
• check_circle Fleet sizing and allocation
• check_circle Demand forecasting and capacity planning
• check_circle Service reliability, on‑time performance, and cost optimization
• check_circle Balance mathematical optimality with operational practicality and change management.
• check_circle Act as the cross‑functional champion for Agentic AI, driving alignment across technical, operational, and leadership teams.
• check_circle Identify opportunities where agent‑based systems can augment planners, dispatchers, analysts, and executives.
• check_circle Design agentic workflows that integrate:
• check_circle + Planning and reasoning Optimization tools and simulation engines Data platforms, APIs, and business rules Human‑in‑the‑loop controls for safety‑critical decisions
• check_circle Optimization tools and simulation engines
• check_circle Data platforms, APIs, and business rules
• check_circle Human‑in‑the‑loop controls for safety‑critical decisions
• check_circle Establish shared standards for governance, observability, safety, and accountability of agentic AI across departments.
• check_circle Partner with data engineering and platform teams to deliver solutions on Microsoft Fabric, including:
• check_circle + OneLake, Lakehouses, and Warehouses Fabric Notebooks (Python / Spark) Power BI semantic models for operational decision support
• check_circle Fabric Notebooks (Python / Spark)
• check_circle Power BI semantic models for operational decision support
• check_circle Ensure analytics and AI outputs are consumable by both technical and non‑technical users.
• check_circle Influence cloud architecture decisions to support real‑time and large‑scale transportation analytics.
• check_circle Bring PhD‑level rigor into applied, cross‑functional problem solving.
• check_circle Translate advances in:
• check_circle + Operations research Machine learning Reinforcement learning Agentic and autonomous systems into solutions that can be operationalized and sustained.
• check_circle Machine learning
• check_circle Reinforcement learning
• check_circle Agentic and autonomous systems into solutions that can be operationalized and sustained.
• check_circle Produce internal frameworks, playbooks, and reference architectures used across teams.
• check_circle Serve as a trusted advisor to senior leaders on:
• check_circle + AI investment decisions Automation risk and readiness Tradeoffs between cost, service quality, and equity
• check_circle Automation risk and readiness
• check_circle Tradeoffs between cost, service quality, and equity
• check_circle Mentor data scientists, analysts, engineers, and operations staff to raise AI literacy across the organization.
• check_circle Facilitate cross‑functional forums or working groups around analytics, AI, and automation.

Basic 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.
• 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
• 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.
• 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.