The Huntington Data Scientist Interview Guide

Getting ready for a Data Scientist interview at The Huntington? The Huntington Data Scientist interview process typically spans multiple question topics and evaluates skills in areas like advanced analytics, machine learning, statistical modeling, and communicating insights to diverse stakeholders. Interview preparation is especially important for this role, as candidates are expected to demonstrate not only technical expertise in building predictive models and working with large-scale data, but also the ability to drive actionable business decisions and present complex findings clearly to both technical and non-technical audiences in a financial services context.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Scientist positions at The Huntington.
• Gain insights into The Huntington’s Data Scientist interview structure and process.
• Practice real The Huntington Data Scientist interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of The Huntington Data Scientist interview process, along with sample questions and preparation tips tailored to help you succeed.

1.2. What The Huntington Does

The Huntington National Bank, commonly known as Huntington, is a leading regional bank headquartered in Columbus, Ohio, serving individuals, small businesses, and commercial clients across the Midwest and beyond. With a focus on customer-centric financial services, Huntington offers a comprehensive suite of banking, lending, investment, and digital solutions. The organization is committed to being the best performing regional bank in America, emphasizing innovation, community engagement, and responsible banking practices. As a Data Scientist at Huntington, you will play a critical role in leveraging advanced analytics and machine learning to drive customer insights, optimize business strategies, and support data-driven decision-making across the enterprise.

As a Data Scientist at The Huntington, you will leverage advanced analytics, machine learning, and statistical modeling to extract actionable insights from large and complex datasets. You will collaborate with teams across the enterprise to optimize digital investments, enhance customer understanding, and support decision-making through data-driven experimentation and reporting. Key responsibilities include developing predictive models, conducting deep research on new data science techniques, creating impactful data visualizations, and presenting findings to senior leadership. This role is instrumental in building the bank’s data infrastructure and advancing its goal to be the best performing regional bank in America by informing strategy and driving innovation across business functions.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application and resume by the Huntington Enterprise Data and Analytics talent acquisition team. They look for demonstrated expertise in machine learning, statistical modeling, experience with large-scale data sets, and hands-on knowledge of tools such as Python, R, SQL, and cloud-based machine learning platforms. Emphasis is placed on experience in predictive modeling, data infrastructure development, and the ability to extract actionable business insights from complex datasets—especially in financial services. To prepare, ensure your resume spotlights relevant projects (e.g., building predictive models, deploying machine learning solutions, data cleaning and organization), quantifies impact, and clearly lists technical proficiencies.

2.2 Stage 2: Recruiter Screen

The recruiter screen is typically a 30-minute call with a Huntington recruiter focused on your background, motivation for applying, and fit for the company’s mission and values. Expect questions about your experience working with both structured and unstructured data, your approach to communicating technical concepts to non-technical stakeholders, and your familiarity with the financial or banking sector. Preparation should include a concise career narrative, specific examples of data-driven impact, and a clear articulation of why you want to work at Huntington.

2.3 Stage 3: Technical/Case/Skills Round

This stage is a deep dive into your technical capabilities and problem-solving approach, usually conducted by a senior data scientist or analytics manager. You may encounter a combination of live coding exercises (often in Python, R, or SQL), case studies, and technical discussions. Topics frequently include building and explaining predictive models, designing data pipelines, evaluating experimental results (e.g., A/B testing for campaign effectiveness), and discussing advanced statistical techniques such as regression, Bayesian inference, or hierarchical modeling. You may be asked to design a data warehouse, explain how you would measure the success of a digital product, or walk through the process of cleaning and transforming messy data. Prepare by reviewing end-to-end data science workflows, practicing model-building and evaluation, and being ready to justify your methodological choices.

2.4 Stage 4: Behavioral Interview

The behavioral round assesses your ability to collaborate across teams, communicate complex findings to varied audiences, and adapt to a dynamic, outcome-driven environment. Panelists may include data science leads, analytics directors, and cross-functional partners from product or business units. Expect scenario-based questions about overcoming hurdles in data projects, presenting insights to executives, and evangelizing best practices. You’ll need to demonstrate intellectual curiosity, strategic thinking, and a track record of influencing business decisions with data. Prepare by reflecting on past projects where you drove change, navigated ambiguity, or mentored peers, and be ready to discuss both successes and setbacks.

2.5 Stage 5: Final/Onsite Round

The final stage often consists of a series of interviews conducted virtually or onsite, involving multiple stakeholders from the data science, analytics, and business teams. This round may include a technical presentation or whiteboarding session where you walk through a recent project, explain your approach to a business problem, or propose a solution to a real-world scenario relevant to Huntington’s operations (e.g., customer segmentation, risk modeling, or campaign analytics). You will be evaluated on your depth of technical knowledge, clarity of communication, and cultural fit. To prepare, select a portfolio project that highlights your end-to-end skills, practice delivering insights to both technical and non-technical audiences, and be ready to answer questions about trade-offs and decision-making.

2.6 Stage 6: Offer & Negotiation

If you advance to this stage, you’ll receive a call from the recruiter to discuss the offer package, which includes base salary, incentive compensation, and benefits. You’ll have the opportunity to negotiate terms and clarify any remaining questions about the role, team structure, or workplace flexibility. Preparation should involve researching compensation benchmarks, understanding Huntington’s benefits, and identifying your priorities for the negotiation.

2.7 Average Timeline

The typical Huntington Data Scientist interview process spans 3–5 weeks from initial application to final offer, with some variation based on candidate availability and scheduling. Fast-track candidates with strong alignment to the company’s technical and business needs may complete the process in as little as two weeks, while the standard pace involves about a week between each stage. Take-home assignments, if included, generally have a 3–5 day completion window, and onsite rounds are scheduled to accommodate both candidate and panelist availability.

Next, let’s explore the types of interview questions you’re likely to encounter throughout the Huntington Data Scientist interview process.

3.1. Machine Learning & Modeling

These questions assess your understanding of core machine learning concepts, practical model building, and communicating technical ideas. Focus on demonstrating a structured approach to problem solving, the ability to explain complex topics clearly, and awareness of real-world constraints.

3.1.1 Build a random forest model from scratch.
Outline the steps for building a random forest, including data preparation, bootstrapping, tree construction, and aggregation of predictions. Highlight your understanding of ensemble methods and how you would evaluate model performance.

3.1.2 Identify requirements for a machine learning model that predicts subway transit.
Discuss your approach to feature selection, handling time-series data, and defining target variables. Emphasize considerations for data sources, model type, and validation strategies.

3.1.3 Building a model to predict if a driver on Uber will accept a ride request or not.
Describe the data you’d collect, relevant features, and model choice. Address how you’d handle class imbalance and evaluate prediction accuracy.

3.1.4 Justify the use of a neural network for a given problem.
Explain the suitability of neural networks for complex, non-linear problems. Compare with alternative models and discuss interpretability and scalability.

3.1.5 Explain the use/s of LDA related to machine learning.
Summarize the purpose of LDA for dimensionality reduction and classification. Illustrate with examples where LDA improves model performance or interpretability.

3.2. Data Analysis & Experimentation

These questions evaluate your ability to design experiments, interpret data, and extract actionable insights. Focus on statistical rigor, real-world relevance, and clear communication of findings.

3.2.1 We're interested in how user activity affects user purchasing behavior.
Describe your approach to segmenting users, defining metrics, and analyzing correlations between activity and purchases. Address potential confounders and how you’d validate the findings.

3.2.2 Precisely ascertain whether the outcomes of an A/B test, executed to assess the impact of a landing page redesign, exhibit statistical significance.
Discuss hypothesis formulation, selection of statistical tests, and interpretation of p-values. Explain how you’d communicate results and implications to stakeholders.

3.2.3 The role of A/B testing in measuring the success rate of an analytics experiment.
Outline the steps for setting up an A/B test, choosing success metrics, and analyzing results. Emphasize the importance of sample size and experiment validity.

3.2.4 How would you present the performance of each subscription to an executive?
Describe your approach to summarizing key metrics, visualizing trends, and highlighting actionable insights. Focus on tailoring the presentation to a non-technical audience.

3.2.5 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Detail your experimental design, key performance indicators, and how you’d measure ROI. Address potential risks and how you’d communicate recommendations.

3.3. Data Engineering & System Design

These questions test your ability to design scalable data systems, ensure data integrity, and optimize data pipelines. Focus on architecture decisions, trade-offs, and practical implementation.

3.3.1 Design a data warehouse for a new online retailer.
Explain your approach to schema design, ETL processes, and accommodating growth. Emphasize considerations for data quality, security, and reporting needs.

3.3.2 System design for a digital classroom service.
Describe the architecture, data flow, and how you’d support analytics and reporting. Highlight scalability, privacy, and integration with existing systems.

3.3.3 Design a database for a ride-sharing app.
Discuss key entities, relationships, and how you’d optimize for query performance. Address data consistency and real-time analytics requirements.

3.3.4 How would you approach improving the quality of airline data?
Detail your process for profiling, cleaning, and monitoring data quality. Explain the importance of automation and ongoing validation.

3.4. Communication & Stakeholder Management

These questions probe your ability to make data accessible, present insights, and collaborate across teams. Focus on storytelling, tailoring messages, and building consensus.

3.4.1 Demystifying data for non-technical users through visualization and clear communication.
Discuss strategies for simplifying complex findings, using intuitive visuals, and adapting explanations for diverse audiences.

3.4.2 How to present complex data insights with clarity and adaptability tailored to a specific audience.
Outline your approach to structuring presentations, anticipating questions, and focusing on business impact.

3.4.3 Making data-driven insights actionable for those without technical expertise.
Explain how you translate technical results into practical recommendations. Emphasize iterative feedback and continuous improvement.

3.4.4 How would you answer when an Interviewer asks why you applied to their company?
Describe how you align your personal values and skills with the company’s mission and culture. Highlight specific aspects of the role or organization that motivate you.

3.5. Data Cleaning & Real-World Challenges

These questions assess your experience with messy, real-world data, troubleshooting, and process improvement. Focus on practical examples, trade-offs, and reproducibility.

3.5.1 Describing a real-world data cleaning and organization project.
Share a specific project, the challenges faced, and the steps taken to clean and structure the data. Highlight tools used and lessons learned.

3.5.2 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Discuss strategies for standardizing formats, handling missing values, and preparing data for analysis. Emphasize the importance of documentation and reproducibility.

3.5.3 Describing a data project and its challenges.
Outline the project goals, obstacles encountered, and your problem-solving approach. Focus on adaptability and stakeholder communication.

3.5.4 Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign.
Explain your approach to conditional aggregation and filtering in SQL. Address performance considerations with large event logs.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe the context, the data you analyzed, your recommendation, and the outcome. Emphasize the impact on business goals.

3.6.2 Describe a challenging data project and how you handled it.
Share the project’s objective, major obstacles, and how you overcame them. Focus on adaptability and resourcefulness.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your approach to clarifying goals, seeking stakeholder input, and iteratively refining your analysis. Highlight communication strategies.

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Describe how you facilitated discussion, presented evidence, and found common ground. Stress collaboration and openness.

3.6.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Share your initial challenges, steps taken to clarify your message, and adjustments made for different audiences.

3.6.6 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Explain how you prioritized requests, communicated trade-offs, and maintained project integrity.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Discuss your strategy for building trust, presenting compelling evidence, and driving consensus.

3.6.8 Describe a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Explain your approach to handling missing data, communicating uncertainty, and ensuring actionable insights.

3.6.9 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe the tools and processes you implemented, the impact on workflow, and how you measured success.

3.6.10 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Explain how visual aids helped bridge gaps in understanding and facilitated productive feedback.

4.1 Company-specific tips:

Immerse yourself in Huntington’s mission to be the best performing regional bank in America. Review their annual reports, press releases, and recent initiatives around digital banking and customer experience. Understand how Huntington leverages data to drive innovation, improve financial products, and deepen customer relationships.

Familiarize yourself with the regulatory and compliance environment of financial services. Huntington operates in a highly regulated sector, so awareness of data privacy, risk management, and ethical use of analytics will set you apart. Be ready to discuss how you would approach sensitive data and ensure compliance in your work.

Research Huntington’s enterprise data strategy. Explore how data science fits into their broader analytics transformation, including investments in cloud platforms, machine learning, and automation. This will help you tailor your answers to the company’s priorities and demonstrate alignment with their goals.

4.2 Role-specific tips:

4.2.1 Practice building and explaining predictive models for financial applications.
Prepare to walk through the full lifecycle of a predictive model—from data exploration and feature engineering to validation and deployment—using examples relevant to banking, such as credit risk scoring, customer segmentation, or fraud detection. Be ready to justify your choice of algorithms, discuss trade-offs, and interpret results for both technical and business audiences.

4.2.2 Strengthen your understanding of advanced statistical techniques and experimental design.
Review concepts such as regression analysis, Bayesian inference, hierarchical modeling, and A/B testing. Practice explaining how you would design experiments to measure the impact of a new product feature or marketing campaign, and how you would communicate statistical significance and business implications to stakeholders.

4.2.3 Prepare to discuss your experience with large-scale data cleaning and organization.
Share specific examples of projects where you tackled messy, incomplete, or inconsistent data. Highlight your approach to profiling, cleaning, and structuring datasets for analysis. Emphasize tools you used (e.g., Python, SQL) and your strategies for ensuring data quality and reproducibility.

4.2.4 Demonstrate your ability to design scalable data pipelines and data warehouses.
Practice describing how you would architect a data warehouse for financial reporting or customer analytics, including schema design, ETL processes, and considerations for data security and compliance. Be ready to discuss how you optimize data pipelines for performance and reliability.

4.2.5 Showcase your skills in communicating complex findings to non-technical stakeholders.
Prepare examples of how you’ve used data visualization and storytelling to make insights accessible and actionable for executives or business partners. Focus on tailoring your message to the audience, anticipating questions, and highlighting the business impact of your work.

4.2.6 Illustrate your approach to stakeholder management and cross-functional collaboration.
Share stories of how you’ve influenced decision-making, navigated ambiguity, or resolved disagreements within project teams. Emphasize your ability to build consensus, present evidence, and adapt to diverse perspectives in a dynamic business environment.

4.2.7 Be ready to discuss ethical considerations and data governance in financial services.
Articulate your understanding of the importance of data privacy, security, and responsible analytics. Be prepared to explain how you would address ethical dilemmas, ensure compliance, and maintain trust with customers and regulators.

4.2.8 Prepare a portfolio project that demonstrates end-to-end data science skills.
Select a recent project that showcases your ability to define business problems, build models, clean and organize data, and deliver actionable insights. Practice presenting your work clearly, discussing challenges faced, and answering questions about your methodological choices and impact.

5.1 “How hard is the The Huntington Data Scientist interview?”
The Huntington Data Scientist interview is considered moderately to highly challenging, especially for those without prior experience in financial services or large-scale analytics. The process tests deep technical expertise in machine learning, statistical modeling, and data engineering, as well as the ability to communicate complex findings to both technical and non-technical stakeholders. Candidates should be prepared for rigorous technical screens, business case discussions, and behavioral interviews focused on real-world problem solving and stakeholder management.

5.2 “How many interview rounds does The Huntington have for Data Scientist?”
Typically, the interview process for a Data Scientist at The Huntington consists of 5-6 rounds. These include an initial application review, recruiter screen, technical/case/skills round, behavioral interview, a final onsite or virtual round (often involving a technical presentation), and the offer/negotiation stage. Each round is designed to assess different aspects of your technical skills, business acumen, and cultural fit.

5.3 “Does The Huntington ask for take-home assignments for Data Scientist?”
Yes, The Huntington often incorporates a take-home assignment into the interview process, particularly for technical roles like Data Scientist. These assignments usually focus on real-world data analysis or modeling tasks relevant to banking and financial analytics. Candidates are typically given 3-5 days to complete the assignment, which is then discussed in subsequent interview rounds.

5.4 “What skills are required for the The Huntington Data Scientist?”
The Huntington seeks Data Scientists with strong proficiency in machine learning, statistical analysis, and data engineering. Key skills include expertise in Python, R, and SQL; experience with predictive modeling, data cleaning, and feature engineering; knowledge of cloud-based analytics platforms; and the ability to design scalable data pipelines. Strong business acumen, especially in financial services, and the ability to communicate insights to diverse stakeholders are also essential.

5.5 “How long does the The Huntington Data Scientist hiring process take?”
The typical hiring process for a Data Scientist at The Huntington spans 3–5 weeks from initial application to final offer. The timeline can vary depending on candidate and interviewer availability, with fast-track candidates occasionally moving through the process in as little as two weeks. Take-home assignments and onsite rounds may add additional scheduling considerations.

5.6 “What types of questions are asked in the The Huntington Data Scientist interview?”
Candidates can expect a blend of technical, analytical, and behavioral questions. Technical questions cover machine learning algorithms, statistical modeling, data cleaning, and system design. Analytical questions often involve case studies or business scenarios relevant to banking, such as customer segmentation, risk modeling, or campaign analytics. Behavioral questions focus on teamwork, communication, and influencing business decisions with data.

5.7 “Does The Huntington give feedback after the Data Scientist interview?”
The Huntington typically provides high-level feedback through recruiters. While detailed technical feedback may be limited due to company policy, candidates can expect to hear about their overall performance and areas for improvement, especially if they progress to the later stages of the interview process.

5.8 “What is the acceptance rate for The Huntington Data Scientist applicants?”
The acceptance rate for Data Scientist roles at The Huntington is competitive, estimated to be in the range of 3–6% for qualified applicants. The company seeks candidates with a strong blend of technical skills, business understanding, and the ability to drive impact in a regulated financial environment.

5.9 “Does The Huntington hire remote Data Scientist positions?”
Yes, The Huntington does offer remote and hybrid opportunities for Data Scientist roles, particularly for candidates with specialized skills or those located outside their primary office regions. However, some positions may require occasional onsite visits for team collaboration, business meetings, or critical project phases.

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