Role overview
Who We Are
ghSMART is a premier leadership advisory firm trusted by CEOs, boards, and investors to solve their most critical leadership and talent decisions. For more than 30 years, we’ve partnered with many of the world’s most influential leaders and organizations to build winning leadership teams and amplify positive impact. Recognized for excellence, ghSMART consistently earns top rankings in industry surveys (e.g., Vault Consulting awards) and is featured in Forbes’ list of America’s Best Management Consulting Firms. Our culture is entrepreneurial and collaborative, with a strong focus on innovation and client success. Our team is made up of nearly 200 extraordinary individuals across the U.S., Europe, and APAC, who become trusted advisors to these leaders, helping amplify their positive impact on the world. We advise on the art and science of building winning leadership teams, doing meaningful work every day.
What You’ll Do
As a Machine Learning & Data Engineer, you will leverage ghSMART’s extensive, structured leadership dataset to build advanced AI solutions/agents and create new RAG‑based solutions. You’ll contribute to the evolution of our ghSMART Leadership Intelligence Platform, help drive research initiatives, and collaborate with academic partners. You will also support both data engineering and AI engineering efforts, ensuring systems, pipelines, and models work cohesively to power leadership insights at scale.
What you'll work on
- Design, build, and extend ML models (LLMs and traditional ML) that deliver high-accuracy insights from ghSMART’s structured leadership dataset; own end-to-end experimentation, evaluation, and deployment.
- Develop RAG-based agents and algorithms to unlock novel leadership insights from our research database.
- Integrate advanced solutions and AI Agents into the Leadership Intelligence Platform and partner cross-functionally to align features with strategic objectives and user needs.
- Optimize data pipelines and workflows to ensure robust, efficient data ingestion, transformation, and model serving across engineering teams.
- Collaborate on research with academic partners and contribute to publications and thought leadership by validating findings with rigorous methods.