AI Enablement Data Scientist — Internal Solutions

HDR, Inc.; Detroit (MI); Presencial; USD 150.000 - 215.000; Jornada completa

Descripción de la vacante

HDR, Inc. is building internal AI capabilities to scale across the Transportation Business Group. The Applied Data Scientist – AI Enablement focuses on designing, developing, and deploying AI solutions, improving data foundations, and removing bottlenecks to enable scalable, repeatable services.

In early phases, work centers on internal delivery; later, it expands to client-facing AI delivery and advisory engagements, leveraging proven solutions for future opportunities.

Formación

• 3+ years experience with data engineering tools and languages (SQL, Power Query, Pandas).
• 3+ years experience with BI tools (Power BI, Tableau, Plotly, Seaborn, Matplotlib).
• 3+ years experience with Python and R for data science.
• Strong writing and communication skills.
• Self-motivated and detail-oriented; able to multitask.
• MS Office proficiency and ability to handle confidential information.
• Six Sigma or similar process improvement certification.
• Experience with cloud platforms (Azure, AWS, GCP).

Responsabilidades

• Design, develop, and deploy AI solutions addressing internal transportation needs.
• Build reusable data pipelines and AI components scalable for future client delivery.
• Manage full model lifecycle including deployment, monitoring, drift detection, retraining.
• Identify data, system, and tooling bottlenecks and propose improvements.
• Collaborate with IT and business stakeholders to translate problems into AI solutions.

Conocimientos

SQL

Power Query

Pandas

Python

R

Power BI

Tableau

Communication

Cloud Platforms

Educación

Bachelor’s degree

Herramientas

Azure

AWS

GCP

Data pipelines

Model deployment

Descripción del empleo

HDR, Inc. is building internal AI capabilities to scale across the Transportation Business Group. The Applied Data Scientist – AI Enablement focuses on designing, developing, and deploying AI solutions, improving data foundations, and removing bottlenecks to enable scalable, repeatable services.

In early phases, work centers on internal delivery; later, it expands to client-facing AI delivery and advisory engagements, leveraging proven solutions for future opportunities.