Data Scientist (Associate)

Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation, and navigate constant change. We're seeking a Data Scientist to join the Data Science & Machine Learning team in our Commercial Digital practice, where you'll conduct advanced analytics and build predictive models that transform how Fortune 500 companies make decisions across Financial Services, Manufacturing, Energy & Utilities, and other commercial industries.

This isn't a reporting role or a dashboard factory—you'll own the full analytics lifecycle from hypothesis formulation through insight delivery. You'll work on problems that matter: experimental designs that validate business strategies, predictive models that surface hidden patterns in complex data, and analytical workflows 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 conduct customer segmentation and lifetime value analysis for a financial services firm, design and analyze a pricing experiment for a global manufacturer, and build an anomaly detection model for a utility company's operational data. If you thrive on rigorous analysis, clear communication of complex findings, and rapid iteration, this role is for you.

• 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.
• Develop 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.
• Rapidly build interactive data stories and applications—deliver insights through compelling visualizations and user-friendly interfaces that stakeholders can explore.
• Translate complex analytical findings into actionable insights—create compelling data narratives, develop presentation-ready deliverables, and communicate technical results to non-technical stakeholders in ways that drive decisions.
• Collaborate directly with clients and senior team members—understand business problems, formulate the right analytical questions, and contribute to insights that create measurable value.

• 2+ 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.
• Strong Python and SQL programming skills with deep experience in the data science ecosystem (Pandas, NumPy, Scikit-learn, statsmodels, visualization libraries). Comfortable writing clean, reproducible code, not just notebooks.
• Solid foundation in statistics and machine learning: hypothesis testing, regression analysis, classification, clustering, experimental design, and understanding of when different approaches are appropriate for different questions.
• Experience with deep learning and modern neural architectures—understanding of transformer models, embeddings, and how to leverage foundation models for analytical tasks. You know when ML approaches add value over classical methods.
• Proficiency with data platforms: Microsoft Fabric, Snowflake, Databricks, or similar cloud analytics environments. You're comfortable working with large datasets and can write efficient queries.
• Strong visualization and rapid data application development skills—proficiency with programmatic visualization libraries (Plotly, Altair) and AI-assisted rapid application development using Cursor, Lovable, v0, or similar tools. You can quickly build interactive data interfaces that bring analyses to life.
• Ability to communicate technical concepts to non-technical stakeholders and work effectively with cross-functional teams. Strong data storytelling skills are essential.
• Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, 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 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), or uncertainty quantification in business contexts.
• 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.

Associate United States of America