Huron Consulting Group Holdings LLC
Machine Learning Engineer
Huron Consulting Group Holdings LLC, Granite Heights, Wisconsin, United States
Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation, and navigate constant change. Through a combination of strategy, expertise, and creativity, the company helps clients accelerate operational, digital, and cultural transformation, enabling the change they need to own their future.
Join the team as the expert you are now and create your future.
The company is seeking a Machine Learning Engineering Manager to join the Data Science & Machine Learning team in the Commercial Digital practice. In this role, you will lead the design, development, and deployment of intelligent systems that solve complex business problems across Financial Services, Manufacturing, Energy & Utilities, and other commercial industries.
Managers play a vibrant, integral role at Huron. Their invaluable knowledge is reflected in the projects they manage and the teams they lead. Known for building long‑standing partnerships with clients, they collaborate with colleagues to solve the most important challenges. Remarkably versatile, Managers also spend significant time mentoring junior staff on the engagement team—sharing expertise, feedback, and encouragement. This promotes a culture of respect, unity, collaboration, and personal achievement.
This is not a research role or a support function—you will own the full ML solution lifecycle from problem definition through production deployment, while leading and developing a team of engineers and data scientists. You will work on systems that matter: forecasting models that inform multi-million‑dollar decisions, agentic AI systems that automate complex workflows, and operational ML solutions that transform how enterprises run. Clients are Fortune 500 companies looking for partners who can deliver, not just advise.
The variety is real. In your first year, you might lead an agentic demand forecasting system for a global manufacturer, oversee an intelligent knowledge processing pipeline for a financial services firm, and architect an energy grid demand simulation model for a utilities company—all while developing the next generation of ML talent at Huron. If you thrive on learning new domains quickly, shipping intelligent production systems, and building high‑performing teams, this role is for you.
What You’ll Do
Lead and mentor junior ML engineers and data scientists — provide technical guidance, conduct code reviews, and support professional development. Foster a culture of continuous learning and high‑quality engineering practices within the team.
Manage complex multi‑workstream ML projects — oversee project planning, resource allocation, and delivery timelines. Ensure projects meet quality standards and client expectations while maintaining technical excellence.
Design and architect end‑to‑end ML solutions—from data pipelines and feature engineering through model training, evaluation, and production deployment. Make key technical decisions and own the overall solution architecture.
Lead development of both traditional ML and generative AI systems, including supervised/unsupervised learning, time‑series forecasting, NLP, LLM applications, RAG architectures, and agent‑based systems using frameworks such as Agent Framework, LangChain, LangGraph, or similar.
Build financial and operational models that drive business decisions—demand forecasting, pricing optimization, risk scoring, anomaly detection, and process automation for commercial enterprises.
Establish MLOps best practices—define and implement CI/CD pipelines, model versioning, monitoring, drift detection, and automated retraining standards to ensure solutions remain reliable in production.
Serve as a trusted advisor to clients—build long‑standing partnerships, understand business problems, translate requirements into technical solutions, and communicate results to both technical and executive audiences.
Contribute to practice development—participate in business development activities, develop reusable assets and methodologies, and help shape the technical direction of Huron’s DSML capabilities.
Required Qualifications
5+ years of hands‑on experience building and deploying ML solutions in production—not just notebooks and prototypes. You have trained models, put them into production, and maintained them at scale.
Experience leading and developing technical teams, including coaching, mentorship, code review, and performance management. Demonstrated ability to build high‑performing teams and develop junior talent.
Solid foundation in ML fundamentals: supervised and unsupervised learning, model evaluation, feature engineering, hyperparameter tuning, and understanding of when different approaches are appropriate.
Experience with cloud ML platforms, particularly Azure Machine Learning, with working knowledge of AWS SageMaker or Google AI Platform. The company is platform‑flexible but Microsoft‑preferred.
Proficiency with data platforms such as SQL, Snowflake, Databricks, or similar. You are comfortable working with large datasets and architecting data pipelines.
Experience with LLMs and generative AI, including prompt engineering, fine‑tuning, embeddings, RAG systems, or agent frameworks. You understand both the capabilities and limitations.
Excellent communication and client management skills—ability to communicate technical concepts to non‑technical stakeholders, lead client meetings, and build trusted relationships with executive audiences.
Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative field (or equivalent practical experience).
Willingness to travel approximately 30% to client sites as needed.
Preferred Qualifications
Experience in Financial Services, Manufacturing, or Energy & Utilities industries.
Background in forecasting, optimization, or financial modeling applications.
Experience with deep learning frameworks such as PyTorch, TensorFlow, fastai, or DeepSpeed.
Experience with MLOps tools such as MLflow and Weights & Biases.
Contributions to open‑source projects or familiarity with open‑source ML tools and frameworks.
Experience building agentic AI systems using Agent Framework (or predecessors), LangChain, LangGraph, CrewAI, or similar frameworks.
Cloud certifications (Azure AI Engineer, AWS ML Specialty, or Databricks ML Associate).
Consulting experience or demonstrated ability to work across multiple domains and adapt quickly to new problem spaces.
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Engineering and Information Technology
Industries Software Development
#J-18808-Ljbffr
Join the team as the expert you are now and create your future.
The company is seeking a Machine Learning Engineering Manager to join the Data Science & Machine Learning team in the Commercial Digital practice. In this role, you will lead the design, development, and deployment of intelligent systems that solve complex business problems across Financial Services, Manufacturing, Energy & Utilities, and other commercial industries.
Managers play a vibrant, integral role at Huron. Their invaluable knowledge is reflected in the projects they manage and the teams they lead. Known for building long‑standing partnerships with clients, they collaborate with colleagues to solve the most important challenges. Remarkably versatile, Managers also spend significant time mentoring junior staff on the engagement team—sharing expertise, feedback, and encouragement. This promotes a culture of respect, unity, collaboration, and personal achievement.
This is not a research role or a support function—you will own the full ML solution lifecycle from problem definition through production deployment, while leading and developing a team of engineers and data scientists. You will work on systems that matter: forecasting models that inform multi-million‑dollar decisions, agentic AI systems that automate complex workflows, and operational ML solutions that transform how enterprises run. Clients are Fortune 500 companies looking for partners who can deliver, not just advise.
The variety is real. In your first year, you might lead an agentic demand forecasting system for a global manufacturer, oversee an intelligent knowledge processing pipeline for a financial services firm, and architect an energy grid demand simulation model for a utilities company—all while developing the next generation of ML talent at Huron. If you thrive on learning new domains quickly, shipping intelligent production systems, and building high‑performing teams, this role is for you.
What You’ll Do
Lead and mentor junior ML engineers and data scientists — provide technical guidance, conduct code reviews, and support professional development. Foster a culture of continuous learning and high‑quality engineering practices within the team.
Manage complex multi‑workstream ML projects — oversee project planning, resource allocation, and delivery timelines. Ensure projects meet quality standards and client expectations while maintaining technical excellence.
Design and architect end‑to‑end ML solutions—from data pipelines and feature engineering through model training, evaluation, and production deployment. Make key technical decisions and own the overall solution architecture.
Lead development of both traditional ML and generative AI systems, including supervised/unsupervised learning, time‑series forecasting, NLP, LLM applications, RAG architectures, and agent‑based systems using frameworks such as Agent Framework, LangChain, LangGraph, or similar.
Build financial and operational models that drive business decisions—demand forecasting, pricing optimization, risk scoring, anomaly detection, and process automation for commercial enterprises.
Establish MLOps best practices—define and implement CI/CD pipelines, model versioning, monitoring, drift detection, and automated retraining standards to ensure solutions remain reliable in production.
Serve as a trusted advisor to clients—build long‑standing partnerships, understand business problems, translate requirements into technical solutions, and communicate results to both technical and executive audiences.
Contribute to practice development—participate in business development activities, develop reusable assets and methodologies, and help shape the technical direction of Huron’s DSML capabilities.
Required Qualifications
5+ years of hands‑on experience building and deploying ML solutions in production—not just notebooks and prototypes. You have trained models, put them into production, and maintained them at scale.
Experience leading and developing technical teams, including coaching, mentorship, code review, and performance management. Demonstrated ability to build high‑performing teams and develop junior talent.
Solid foundation in ML fundamentals: supervised and unsupervised learning, model evaluation, feature engineering, hyperparameter tuning, and understanding of when different approaches are appropriate.
Experience with cloud ML platforms, particularly Azure Machine Learning, with working knowledge of AWS SageMaker or Google AI Platform. The company is platform‑flexible but Microsoft‑preferred.
Proficiency with data platforms such as SQL, Snowflake, Databricks, or similar. You are comfortable working with large datasets and architecting data pipelines.
Experience with LLMs and generative AI, including prompt engineering, fine‑tuning, embeddings, RAG systems, or agent frameworks. You understand both the capabilities and limitations.
Excellent communication and client management skills—ability to communicate technical concepts to non‑technical stakeholders, lead client meetings, and build trusted relationships with executive audiences.
Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative field (or equivalent practical experience).
Willingness to travel approximately 30% to client sites as needed.
Preferred Qualifications
Experience in Financial Services, Manufacturing, or Energy & Utilities industries.
Background in forecasting, optimization, or financial modeling applications.
Experience with deep learning frameworks such as PyTorch, TensorFlow, fastai, or DeepSpeed.
Experience with MLOps tools such as MLflow and Weights & Biases.
Contributions to open‑source projects or familiarity with open‑source ML tools and frameworks.
Experience building agentic AI systems using Agent Framework (or predecessors), LangChain, LangGraph, CrewAI, or similar frameworks.
Cloud certifications (Azure AI Engineer, AWS ML Specialty, or Databricks ML Associate).
Consulting experience or demonstrated ability to work across multiple domains and adapt quickly to new problem spaces.
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Engineering and Information Technology
Industries Software Development
#J-18808-Ljbffr