Machine Learning Engineer (Manager)

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, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future. 
Join our team as the expert you are now and create your future.

Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation, and navigate constant change. We're seeking a Machine Learning Engineering Manager to join the Data Science & Machine Learning team in our Commercial Digital practice, where you'll 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 reflects in the projects they manage and the teams they lead. Known for building long-standing partnerships with clients, they collaborate with colleagues to solve their most important challenges. Remarkably versatile, our 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 isn't a research role or a support function—you'll own the full ML solution lifecycle from problem definition through production deployment, while leading and developing a team of engineers and data scientists. You'll 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. Our 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.

• 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 like 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.

• 5+ years of hands-on experience building and deploying ML solutions in production—not just notebooks and prototypes. You've 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.
• Strong Python and JavaScript programming skills with deep experience in the ML ecosystem (NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow, etc.) and proficiency with JavaScript web app development.
• 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. We're platform-flexible but Microsoft-preferred.
• Proficiency with data platforms: SQL, Snowflake, Databricks, or similar. You're comfortable working with large datasets and architecting data pipelines.
• Experience with LLMs and generative AI: 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 related quantitative field (or equivalent practical experience).
• Willingness to travel approximately 30% to client sites as needed.

• 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, DeepSpeed, etc.
• 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.
• Master's degree or PhD in a quantitative field.

Variety that accelerates your growth. In consulting, you'll work across industries and problem types that would take a decade to encounter at a single company. Our Commercial segment spans Financial Services, Manufacturing, Energy & Utilities, and more—each engagement is a new domain to master and a new system to ship.

Impact you can measure. Our clients are Fortune 500 companies making significant investments in AI. The models you build will inform real decisions—production schedules, pricing strategies, risk assessments, capital allocation. You'll see your work drive outcomes.

A team that builds. Huron's Data Science & Machine Learning team is a close-knit group of practitioners, not just advisors. We write code, train models, and deploy systems. You'll work alongside engineers and data scientists who understand the craft and push each other to improve.

Investment in your development. We provide resources for continuous learning, conference attendance, and certification. As our DSML practice grows, there's significant opportunity to take on technical leadership, shape our capabilities, and advance to senior leadership roles.