Applied Data Scientist: AI Enablement for Transportation

HDR; Lafayette (LA); On-site; USD 124,000 - 211,000; Full time

Benefits offered by this job

Comprehensive benefits

Employee ownership culture

Job summary

HDR is seeking an Applied Data Scientist – AI Enablement to build internal AI capabilities and remove barriers to scale AI across HDR’s Transportation Business Group. The role starts internally, creating AI solutions, improving data foundations, and identifying bottlenecks, with progression toward client-facing advisory work over time.

Responsibilities include designing reusable AI components, data pipelines, and model lifecycles; identifying data availability and governance gaps; and

Qualifications

• Bachelor’s degree or equivalent in a related field with relevant experience.
• Hands-on experience with generative AI and modern AI services.
• Experience building, deploying, and maintaining cloud-based data/AI solutions.
• Experience managing end-to-end data science projects and model lifecycles.
• Strong written and verbal communication, analytical skills.

Responsibilities

• Internal AI capability development for internal needs and processes.
• Identify data, system, and governance bottlenecks to scale AI.
• Collaborate with IT, data architects, and stakeholders to implement improvements.
• Prepare for future client-facing AI delivery and repeatable services.

Skills

Generative AI

AI solution development

Data analysis

Model lifecycle management

Cross-functional collaboration

Education

Bachelor’s degree in a closely related field

Advanced Degree

Tools

Azure

AWS

GCP

Power BI

Tableau

Python

R

SQL

Job description

HDR is seeking an Applied Data Scientist – AI Enablement to build internal AI capabilities and remove barriers to scale AI across HDR’s Transportation Business Group. The role starts internally, creating AI solutions, improving data foundations, and identifying bottlenecks, with progression toward client-facing advisory work over time.

Responsibilities include designing reusable AI components, data pipelines, and model lifecycles; identifying data availability and governance gaps; and