Data Science 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.

We're seeking a Data Science Manager to join the Data Science & Machine Learning team in our Commercial Digital practice, where you'll lead advanced analytics initiatives that transform how Fortune 500 companies make decisions 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. 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 reporting role or a dashboard factory—you'll own the full analytics lifecycle from hypothesis formulation through insight delivery, while leading and developing a team of data scientists and analysts. You'll work on problems that matter: experimental designs that validate multi-million-dollar strategies, predictive models that surface hidden patterns in complex data, and deep learning pipelines that extract signal from unstructured text, images, and time-series. Our clients are Fortune 500 companies looking for partners who can find the signal in the noise and tell the story that drives action.

The variety is real. In your first year, you might lead a customer segmentation and lifetime value analysis for a financial services firm, design and analyze a pricing experiment for a global manufacturer, and build an agentic anomaly detection system for a utility company's operational data—all while developing the next generation of data science talent at Huron. If you thrive on rigorous analysis, clear communication of complex findings, and building high-performing teams, this role is for you.

What You'll Do

Lead and mentor junior data scientists and analysts—provide technical guidance, review analytical approaches and code, and support professional development. Foster a culture of intellectual curiosity, rigorous methodology, and clear communication within the team.
Manage complex multi-workstream analytics projects—oversee project planning, resource allocation, and delivery timelines. Ensure analyses meet quality standards and client expectations while maintaining methodological rigor.
Design and execute end-to-end data science workflows—from problem framing and hypothesis development through exploratory analysis, modeling, validation, and insight delivery. Own the analytical approach and ensure conclusions are defensible.
Lead development of both traditional statistical and modern AI-powered analyses—including regression, classification, clustering, causal inference, A/B testing, and modern deep learning approaches using embeddings, transformer architectures, and foundation models for text, time-series, and multimodal analysis.
Build predictive and prescriptive models that drive business decisions—customer segmentation, churn prediction, demand forecasting, pricing optimization, risk scoring, and operational efficiency analysis for commercial enterprises.
Translate complex analytical findings into actionable insights—create compelling data narratives, develop executive-ready presentations, and communicate technical results to non-technical stakeholders in ways that drive decisions.
Serve as a trusted advisor to clients—build long-standing partnerships, deeply understand business problems, formulate the right analytical questions, and deliver insights that create measurable value.
Contribute to practice development—participate in business development activities, develop reusable analytical frameworks and methodologies, and help shape the technical direction of Huron's DSML capabilities.

Required Qualifications

5+ years of hands-on experience conducting data science and advanced analytics—not just ad-hoc analysis, but structured analytical projects that drove business decisions. You've framed problems, developed hypotheses, analyzed data, and delivered insights that created measurable impact.
Experience leading and developing technical teams—including coaching, mentorship, methodology review, and performance management. Demonstrated ability to build high-performing teams and develop junior talent.
Strong Python and SQL programming skills with deep experience in the data science ecosystem (Pandas, NumPy, Scikit-learn, statsmodels, visualization libraries). Comfortable writing production-quality code, not just notebooks.
Solid foundation in statistics and machine learning: hypothesis testing, regression analysis, classification, clustering, experimental design, causal inference, and understanding of when different approaches are appropriate for different questions.
Experience with deep learning and modern neural architectures—understanding of transformer models, embeddings, transfer learning, and how to leverage foundation models for analytical tasks. You know when ML approaches add value over classical methods, and how to integrate them into rigorous analytical workflows.
Proficiency with data platforms: Microsoft Fabric, Snowflake, Databricks, or similar cloud analytics environments. You're comfortable working with large datasets and can optimize queries for performance.
Exceptional communication and data storytelling skills—ability to distill complex analyses into clear narratives, create compelling visualizations, lead client meetings, and build trusted relationships with executive audiences. This is non-negotiable.
Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, or related quantitative field (or equivalent practical experience).
Flexibility to work in a hybrid model with periodic travel to client sites as needed.

Preferred Qualifications

Experience in Financial Services, Manufacturing, or Energy & Utilities industries.
Background in experimental design, A/B testing, and causal inference methodologies—including propensity score matching, difference-in-differences, or instrumental variables.
Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) and neural architectures—including transformers, attention mechanisms, and fine-tuning pretrained models for NLP, time-series, or tabular data applications.
Experience building AI-assisted analytical workflows—leveraging foundation model APIs, vector databases, and retrieval systems to accelerate insight extraction from unstructured data.
Experience with Bayesian methods, probabilistic programming (PyMC, NumPyro, etc.), or uncertainty quantification in business contexts.
Strong visualization and data interface design and development skills using programmatic visualization libraries (Plotly, Altair, D3). Proficiency with AI-assisted rapid data application development using Cursor, Lovable, v0, etc.
Experience with time-series analysis, forecasting methods (ARIMA, Prophet, neural forecasting), and demand planning applications.
Cloud certifications (Azure Data Scientist, Databricks ML Associate, AWS ML Specialty).
Consulting experience or demonstrated ability to work across multiple domains and adapt quickly to new problem spaces.
Master's degree or PhD in Statistics, Applied Mathematics, Economics, or related quantitative field.

Why Huron

Variety that accelerates your growth. In consulting, you'll work across industries and analytical challenges 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 problem to crack.

Impact you can measure. Our clients are Fortune 500 companies making significant investments in analytics and AI. The insights you generate will inform real decisions—pricing strategies, customer segmentation, operational improvements, strategic investments. You'll see your analysis drive outcomes.

A team that thinks deeply. Huron's Data Science & Machine Learning team is a close-knit group of practitioners, not just advisors. We develop hypotheses, analyze data, and deliver insights that hold up to scrutiny. You'll work alongside data scientists and engineers who care about getting the answer right and telling the story clearly.

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.

Position Level

Manager

Country

United Kingdom

Details

Location

Belfast, UK

Experience

Min. 5 years5+ years of hands-on experience conducting data science and advanced analytics

Join our team as the expert you are now and create your future.

Data Science Manager

What You'll Do

Lead and mentor junior data scientists and analysts—provide technical guidance, review analytical approaches and code, and support professional development. Foster a culture of intellectual curiosity, rigorous methodology, and clear communication within the team.
Manage complex multi-workstream analytics projects—oversee project planning, resource allocation, and delivery timelines. Ensure analyses meet quality standards and client expectations while maintaining methodological rigor.
Design and execute end-to-end data science workflows—from problem framing and hypothesis development through exploratory analysis, modeling, validation, and insight delivery. Own the analytical approach and ensure conclusions are defensible.
Lead development of both traditional statistical and modern AI-powered analyses—including regression, classification, clustering, causal inference, A/B testing, and modern deep learning approaches using embeddings, transformer architectures, and foundation models for text, time-series, and multimodal analysis.
Build predictive and prescriptive models that drive business decisions—customer segmentation, churn prediction, demand forecasting, pricing optimization, risk scoring, and operational efficiency analysis for commercial enterprises.
Translate complex analytical findings into actionable insights—create compelling data narratives, develop executive-ready presentations, and communicate technical results to non-technical stakeholders in ways that drive decisions.
Serve as a trusted advisor to clients—build long-standing partnerships, deeply understand business problems, formulate the right analytical questions, and deliver insights that create measurable value.
Contribute to practice development—participate in business development activities, develop reusable analytical frameworks and methodologies, and help shape the technical direction of Huron's DSML capabilities.

Required Qualifications

5+ years of hands-on experience conducting data science and advanced analytics—not just ad-hoc analysis, but structured analytical projects that drove business decisions. You've framed problems, developed hypotheses, analyzed data, and delivered insights that created measurable impact.
Experience leading and developing technical teams—including coaching, mentorship, methodology review, and performance management. Demonstrated ability to build high-performing teams and develop junior talent.
Strong Python and SQL programming skills with deep experience in the data science ecosystem (Pandas, NumPy, Scikit-learn, statsmodels, visualization libraries). Comfortable writing production-quality code, not just notebooks.
Solid foundation in statistics and machine learning: hypothesis testing, regression analysis, classification, clustering, experimental design, causal inference, and understanding of when different approaches are appropriate for different questions.
Experience with deep learning and modern neural architectures—understanding of transformer models, embeddings, transfer learning, and how to leverage foundation models for analytical tasks. You know when ML approaches add value over classical methods, and how to integrate them into rigorous analytical workflows.
Proficiency with data platforms: Microsoft Fabric, Snowflake, Databricks, or similar cloud analytics environments. You're comfortable working with large datasets and can optimize queries for performance.
Exceptional communication and data storytelling skills—ability to distill complex analyses into clear narratives, create compelling visualizations, lead client meetings, and build trusted relationships with executive audiences. This is non-negotiable.
Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, or related quantitative field (or equivalent practical experience).
Flexibility to work in a hybrid model with periodic travel to client sites as needed.

Preferred Qualifications

Experience in Financial Services, Manufacturing, or Energy & Utilities industries.
Background in experimental design, A/B testing, and causal inference methodologies—including propensity score matching, difference-in-differences, or instrumental variables.
Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) and neural architectures—including transformers, attention mechanisms, and fine-tuning pretrained models for NLP, time-series, or tabular data applications.
Experience building AI-assisted analytical workflows—leveraging foundation model APIs, vector databases, and retrieval systems to accelerate insight extraction from unstructured data.
Experience with Bayesian methods, probabilistic programming (PyMC, NumPyro, etc.), or uncertainty quantification in business contexts.
Strong visualization and data interface design and development skills using programmatic visualization libraries (Plotly, Altair, D3). Proficiency with AI-assisted rapid data application development using Cursor, Lovable, v0, etc.
Experience with time-series analysis, forecasting methods (ARIMA, Prophet, neural forecasting), and demand planning applications.
Cloud certifications (Azure Data Scientist, Databricks ML Associate, AWS ML Specialty).
Consulting experience or demonstrated ability to work across multiple domains and adapt quickly to new problem spaces.
Master's degree or PhD in Statistics, Applied Mathematics, Economics, or related quantitative field.