ML Engineering 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. The projects benefit the sectors of Financial Services, Manufacturing, Energy & Utilities as we enable the clients the capability to own their future.

Join our team as the expert you are now—create your future—in the following role:

ML Engineering Manager

In Huron’s Data Science & Machine Learning team, under the Commercial Digital practice, this role will encompass leading the design, development, and deployment of intelligent systems. Such systems thrive to solve complex business problems across various industrial sectors, driving rapid operational and strategic initiatives.

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About the Role

Huron managers play a vital role, seamlessly integrating vibrant knowledge into every project while leading teams. Our managers don’t just serve as advisors—they build long-standing partnerships with clients, tackling critical challenges with innovation. They consistently foster mentorship for junior colleagues, creating a culture of respect, unity, collaboration, and personal achievement.

This is not a research role or a support function—you’ll be responsible for owning the full ML solution lifecycle, from problem definition to production deployment, while leading and developing a team of engineers and data scientists.

Your impact spans from:

• Forecasting models that inform multi-million-dollar decisions
• Agentic AI systems automating complex workflows
• Operational ML solutions transforming how enterprises operate

Your expertise will generate real results for Fortune 500 clients seeking deliverables and partnerships.

What the Role Offers

You’ll work across varied and impactful projects within your first year:

• Lead a demand forecasting system for a global manufacturer
• Oversee an intelligent knowledge processing pipeline for a financial services firm
• Architect an energy grid demand simulation model for a utilities company

At the same time, you’ll develop the next generation of ML talent within Huron if you:

• Thrived on learning new domains quickly
• Prefer shipping intelligent production systems
• Drive high-performing teams

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Responsibilities

• Lead and mentor junior ML engineers and data scientists—provide technical guidance, conduct code reviews, and support professional development. Foster an environment of continuous learning and high-quality engineering practices.
• Manage complex multi-workstream ML projects—oversee project planning, resource allocation, and delivery timelines while ensuring solutions meet quality standards and client expectations.
• Design and architect end-to-end ML solutions—from data pipelines and feature engineering, through model training and evaluation, to production deployment. Make technical decisions and own the solution architecture.
• Lead the 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 in frameworks such as Agentic Framework, LangChain, or LangGraph.
• Construct financial and operational models that drive business decisions, such as demand forecasting, pricing optimization, risk scoring, anomaly detection, and process automation.
• Establish and uphold MLOps best practices, such as defining and implementing CI/CD pipelines, model versioning, monitoring, drift detection, features retraining to ensure reliable solutions in production.
• Serve as a trusted advisor to clients, fostering long-standing partnerships that consider their business problems and then translate requirements into technical solutions. Communicate results clearly to both technical and executive audiences.
• Contribute to practice development, assisting in business development activities, developing reusable assets, refining methodologies, and shaping Huron’s technical direction in the DSML sector.