Associate Data Scientist, People Analytics

Texas A&M Transportation Institute; Bryan (TX); Hybrid; USD 65,000 - 90,000; Full time

Job summary

Texas A&M Transportation Institute (TTI) seeks an Assistant Data Scientist in People Analytics to design analytics solutions, dashboards, and automated reporting pipelines. You will support HR operations, Laserfiche processes, and AI initiatives, partnering with HR and other units to analyze complex data and drive improvements.

The role emphasizes hands-on data prep, visualization, and deployment of predictive models, with hybrid work arrangements and a focus on safeguarding confidential data.

Qualifications

• Bachelor’s Degree with at least 2 years of related experience.
• Experience with data visualization tools such as Power BI is preferred.
• Experience administering Laserfiche or similar workflow platforms is a plus.
• Experience with APIs and data integrations is desirable.
• Experience with AI initiatives or automation is a plus.

Responsibilities

• Develop automated scripts and workflows to streamline HR processes using Power Automate and Laserfiche.
• Build and maintain dashboards with Power BI to track HR KPIs and metrics.
• Develop, deploy, and support AI-enabled solutions to improve processes and decision-making.
• Collaborate with stakeholders to gather requirements and translate them into automated solutions.
• Ensure data security and governance in AI and automation projects.

Skills

Analytical thinking

Communication

Problem solving

Data interpretation

Education

Bachelor’s Degree in related field

Tools

Power BI

Laserfiche

Power Query

Power Automate

APIs

Job description

Texas A&M Transportation Institute (TTI) seeks an Assistant Data Scientist in People Analytics to design analytics solutions, dashboards, and automated reporting pipelines. You will support HR operations, Laserfiche processes, and AI initiatives, partnering with HR and other units to analyze complex data and drive improvements.

The role emphasizes hands-on data prep, visualization, and deployment of predictive models, with hybrid work arrangements and a focus on safeguarding confidential data.