1. What is a Research Analyst at and Huntington?

Stepping into the role of a Research Analyst—specifically operating as a Commercial Bank Data and Insights Analyst—at and Huntington means becoming a crucial driver of data-informed strategy within the commercial banking sector. This position is central to how the bank understands its commercial clients, optimizes its lending portfolios, and identifies new revenue opportunities. You will be transforming raw financial and operational data into actionable insights that directly influence high-level business decisions.

Your impact in this role extends across multiple product lines and teams based out of the Columbus, OH headquarters and beyond. By analyzing market trends, client behavior, and internal performance metrics, you empower commercial banking leaders to refine their go-to-market strategies and improve client experiences. The scale of the data is massive, and the complexity of commercial banking products requires a sharp analytical mind capable of navigating ambiguity.

Expect a highly collaborative, fast-paced environment where your findings will be heavily scrutinized and relied upon. The and Huntington team values analysts who do not just pull numbers, but who can craft a compelling narrative around what those numbers mean for the business. You will be expected to act as a strategic partner, bridging the gap between deep technical data extraction and executive-level business strategy.

2. Getting Ready for Your Interviews

Preparing for your interview requires a balanced approach, as and Huntington evaluates candidates on both their technical rigor and their commercial banking intuition. You should approach your preparation by mastering a few core competencies that interviewers will consistently test.

Technical & Data Fluency – This evaluates your ability to extract, manipulate, and visualize complex datasets. Interviewers at and Huntington want to see your proficiency in SQL, Excel, and visualization tools like Tableau or PowerBI. You can demonstrate strength here by clearly explaining your data-cleaning processes and how you ensure data integrity before running analyses.

Commercial Banking Acumen – This criterion measures your understanding of the financial services industry, specifically commercial lending, treasury management, and risk. Interviewers evaluate this by asking how you would approach specific banking scenarios. Strong candidates will proactively use banking terminology and show an understanding of how macroeconomic factors impact commercial clients.

Problem-Solving Ability – This assesses how you structure ambiguous business questions into logical, data-driven frameworks. You will be evaluated on your analytical methodology rather than just getting the "right" answer. Demonstrate this by thinking out loud, outlining your assumptions, and explaining the step-by-step logic you would use to solve a case study.

Stakeholder Communication – This looks at your capacity to translate complex data into simple, actionable insights for non-technical banking executives. Interviewers will test this through behavioral questions and presentation exercises. You can excel here by using the STAR method and focusing heavily on the business impact of your past analytical projects.

3. Interview Process Overview

The interview process for the Commercial Bank Data and Insights Analyst role at and Huntington is designed to be thorough, testing both your technical capabilities and your cultural alignment with the bank. Typically, the process begins with an initial HR phone screen, which is conversational and focuses on your background, salary expectations, and basic qualifications. This is followed by a hiring manager interview, which dives deeper into your resume and assesses your high-level understanding of commercial banking data.

If you pass the initial screens, you will usually face a technical assessment or a take-home data case study. This stage is highly pragmatic, asking you to perform data manipulation, write SQL queries, or build a basic dashboard using a sample dataset representative of actual and Huntington commercial banking data. The final round consists of a panel interview where you will present your case study findings and face a mix of behavioral, technical, and situational questions from various stakeholders, including product managers and senior analysts.

Throughout the process, the bank places a heavy emphasis on collaboration and user focus. Interviewers want to see how you handle pushback on your data insights and how effectively you can partner with commercial bankers who may not have a technical background. The rigor is high, but the atmosphere is generally supportive and focused on discovering how you think rather than trying to trick you with obscure technical trivia.

This visual timeline outlines the typical progression of the and Huntington interview process, from the initial recruiter screen through the technical assessment and final panel rounds. You should use this to pace your preparation, focusing first on your behavioral narrative and banking acumen before diving deeply into SQL and case study practice for the later stages. Keep in mind that the exact sequence may vary slightly depending on the specific commercial banking team you are interviewing with.

4. Deep Dive into Evaluation Areas

To succeed in securing the Research Analyst position, you must deeply understand the core areas where you will be evaluated. The and Huntington hiring team uses these areas to separate capable number-crunchers from true strategic partners.

Data Manipulation and SQL

This area is foundational because you cannot generate insights without first accurately extracting and structuring the data. Interviewers will test your ability to write efficient queries, join complex tables, and handle messy, incomplete datasets typical of legacy banking systems. Strong performance means writing clean, optimized code and explaining your logic clearly.

Be ready to go over:

• Complex Joins and Aggregations – Understanding when to use inner vs. left joins and how to group financial data by client or region.
• Window Functions – Using functions like RANK(), LEAD(), and LAG() to analyze month-over-month changes in client account balances.
• Data Cleaning – Identifying and handling null values, duplicates, or outliers in transactional data.
• Advanced concepts (less common) –
  • Query optimization and execution plans.
  • Writing stored procedures or dynamic SQL.
  • Basic Python or R for statistical analysis.

Example questions or scenarios:

• "Write a SQL query to find the top 5 commercial clients by total loan volume, excluding any clients with a high-risk flag."
• "How would you handle a dataset where 20% of the commercial transaction records are missing the industry classification code?"
• "Explain a time when your SQL query was running too slowly and what steps you took to optimize it."

Commercial Banking Domain Knowledge

Understanding the business context is just as critical as your technical skills. This area evaluates your familiarity with how a commercial bank operates, makes money, and manages risk. Strong candidates easily connect data points to real-world banking outcomes, such as loan origination, deposit growth, and treasury management services.

Be ready to go over:

• Key Performance Indicators (KPIs) – Familiarity with metrics like Net Interest Margin (NIM), Loan-to-Deposit Ratio, and Return on Equity (ROE).
• Client Segmentation – Understanding how commercial clients are grouped by revenue, industry, and risk profile.
• Product Knowledge – Basic understanding of commercial loans, lines of credit, and treasury management products.
• Advanced concepts (less common) –
  • Regulatory reporting requirements (e.g., Basel III, stress testing).
  • Macroeconomic impact on interest rates and commercial lending.

Example questions or scenarios:

• "If you noticed a sudden 15% drop in commercial deposit balances across the Midwest region, how would you investigate the cause?"
• "What data points would you look at to determine if a new treasury management product is successful?"
• "Explain how rising interest rates might impact our commercial lending portfolio and what data you would track to monitor this."

Data Visualization and Storytelling

Having the right data is useless if you cannot communicate it effectively. This area tests your ability to design intuitive dashboards and present findings in a way that drives executive action. Interviewers look for candidates who prioritize clarity, user experience, and a strong narrative arc over overly complex, cluttered visuals.

Be ready to go over:

• Dashboard Design Principles – Choosing the right chart types for financial data and avoiding visual clutter.
• Stakeholder Empathy – Tailoring your presentation style and level of detail to the audience (e.g., a commercial banker vs. a data engineer).
• Actionable Insights – Moving beyond "what happened" to explain "why it happened" and "what we should do next."
• Advanced concepts (less common) –
  • Predictive modeling visualizations.
  • Advanced DAX (for PowerBI) or Level of Detail (LOD) expressions (for Tableau).

Example questions or scenarios:

• "Walk me through a dashboard you built from scratch. Who was the end-user, and what business problem did it solve?"
• "How do you handle a situation where a senior executive disagrees with the data insights you are presenting?"
• "If you have to present complex risk modeling data to a non-technical sales team, how do you structure your presentation?"

5. Key Responsibilities

As a Commercial Bank Data and Insights Analyst at and Huntington, your day-to-day work revolves around turning complex financial data into a strategic asset. Your primary responsibility is to design, develop, and maintain reporting solutions and dashboards that track the performance of commercial banking products. You will spend a significant portion of your week extracting data from internal data warehouses using SQL, cleaning it, and feeding it into visualization tools like Tableau or PowerBI to monitor key metrics such as loan origination volume and deposit trends.

Collaboration is a massive part of this role. You will frequently partner with commercial bankers, product managers, and risk officers to understand their specific data needs. Instead of just taking orders, you are expected to act as a consultant—helping them refine their business questions and suggesting the best analytical approaches. You will also work closely with data engineering teams to ensure that the data pipelines feeding your reports are accurate, reliable, and up-to-date.

Additionally, you will drive ad-hoc analytical projects to support strategic initiatives. This might involve deep-dive research into specific market segments, analyzing the success of a recent commercial product launch, or identifying cross-selling opportunities within the existing client base. Your deliverables will often be presented in executive summaries, meaning you must constantly distill your complex data work into clear, concise, and actionable business recommendations.

6. Role Requirements & Qualifications

To be highly competitive for the Research Analyst position at and Huntington, candidates must present a strong blend of technical hard skills and business-facing soft skills. The hiring team looks for individuals who can operate independently while maintaining strong alignment with broader commercial banking goals.

• Must-have technical skills – Advanced proficiency in SQL for data extraction and manipulation. Deep expertise in Excel (pivot tables, complex formulas, modeling). Strong experience with BI visualization tools, specifically Tableau or PowerBI.
• Must-have soft skills – Exceptional stakeholder management and communication abilities. You must be able to translate technical findings into business strategy. Strong problem-solving frameworks to tackle ambiguous, open-ended business questions.
• Experience level – Typically, this role requires 3 to 5 years of experience in data analytics, business intelligence, or financial research. A background specifically in banking, financial services, or consulting is highly preferred.
• Nice-to-have skills – Experience with Python or R for statistical analysis and automation. Familiarity with cloud data platforms (e.g., Snowflake, AWS). Understanding of commercial banking products, treasury management, or credit risk modeling.

7. Common Interview Questions

The following questions are representative of what candidates face during the and Huntington interview process. While you should not memorize answers, you should use these to recognize patterns and practice structuring your responses.

SQL and Data Manipulation

These questions test your technical ability to retrieve and organize data accurately. Focus on explaining your logic before writing the code.

• How do you write a query to find the second-highest loan amount in a commercial client table?
• Explain the difference between a WHERE clause and a HAVING clause, and give an example of when you would use each.
• How would you identify and remove duplicate transaction records from a massive dataset?
• Walk me through how you would use a window function to calculate a rolling 3-month average for client deposits.
• Describe a time when you had to merge data from two completely different legacy systems. How did you ensure data integrity?

Business and Commercial Banking Acumen

These questions evaluate your understanding of the industry and how data drives financial decisions.

• What key metrics would you include in a dashboard designed for a Commercial Lending Director?
• How would you analyze our data to identify clients who are a flight risk to a competitor bank?
• If commercial loan originations are up, but overall revenue is down, what data points would you investigate to explain the discrepancy?
• Explain a commercial banking concept (like Net Interest Margin) to me as if I have no financial background.
• How does a changing macroeconomic environment (like inflation) change the way you analyze commercial credit risk?

Behavioral and Scenario-Based

These questions assess your soft skills, stakeholder management, and cultural fit within and Huntington.

• Tell me about a time your data analysis led to a significant change in business strategy.
• Describe a situation where you found a critical error in your data right before a major presentation. How did you handle it?
• How do you prioritize your work when multiple commercial banking executives are demanding ad-hoc reports at the same time?
• Give an example of a time you had to push back on a stakeholder who was misinterpreting your data.
• Tell me about a time you had to learn a complex new business domain or technical tool very quickly to complete a project.

8. Frequently Asked Questions

Q: How difficult is the technical assessment for this role? The technical assessment is rigorous but fair, focusing on practical application rather than trick questions. If you are comfortable writing complex SQL joins, using window functions, and building clean visual dashboards from scratch, you will be well-prepared. Spend a few days refreshing your syntax and practicing on financial datasets.

Q: What differentiates a good candidate from a great candidate at and Huntington? A good candidate can pull the data and build the report perfectly. A great candidate takes the initiative to ask why the report is needed, identifies the underlying business problem, and uses the data to recommend a specific, actionable strategy to the commercial banking team.

Q: What is the typical timeline from the initial screen to an offer? The process typically takes between 3 to 5 weeks. After the initial HR screen, you can expect the hiring manager interview within a week, followed by the technical assessment and final panel over the subsequent two to three weeks.

Q: What is the working style and culture like for this specific team? The Commercial Bank Data and Insights team operates with a high degree of collaboration and a strong focus on internal client service. The culture is professional and data-driven, requiring analysts to be highly adaptable and comfortable presenting to senior leadership in the Columbus, OH office.

Q: Are these roles typically remote, hybrid, or fully in-office? Roles based out of the Columbus, OH headquarters typically follow a hybrid model, requiring candidates to be in the office a few days a week. It is important to clarify the exact in-office expectations with your recruiter during the initial phone screen.

9. Other General Tips

• Master the STAR Method: When answering behavioral questions, strictly follow the Situation, Task, Action, Result format. At and Huntington, interviewers place heavy emphasis on the "Result" portion—always quantify the business impact of your actions.
• Speak the Language of Banking: Incorporate commercial banking terminology naturally into your case studies and answers. Using terms like "portfolio yield," "treasury management," and "credit risk" shows you already understand their world.
• Think Out Loud During Technical Cases: If you are given a live case study or technical problem, do not just stare at the screen in silence. Narrate your thought process, explain your assumptions, and discuss edge cases you are considering.
• Focus on the "So What?": Never present a data point without context. Whenever you share an insight during your interviews, immediately follow it up by explaining why it matters to the business and what action should be taken.
• Prepare Insightful Reverse Questions: At the end of your interviews, ask questions that show you are thinking strategically. Ask about the biggest data challenges the commercial banking team is currently facing, or how data is currently being used to drive new product innovation.

10. Summary & Next Steps

Securing a Research Analyst role within the Commercial Bank Data and Insights team at and Huntington is a highly rewarding achievement. This position offers the opportunity to sit at the intersection of complex data engineering and high-level financial strategy. You will be doing work that directly influences the bank's commercial success, making your role highly visible and deeply impactful.

The provided salary module highlights the compensation range of $64,972 -$102,546 USD for this specific position in Columbus, OH. Where you fall within this band will depend heavily on your years of specialized banking experience, your technical proficiency with SQL and BI tools, and your performance during the case study rounds. Use this data to anchor your expectations and negotiate confidently when the time comes.

To succeed, focus your preparation on bridging the gap between technical data extraction and strategic business storytelling. Review your SQL syntax, practice building intuitive dashboards, and ensure you can articulate the business value of your past projects clearly. Remember that the interviewers are looking for a trusted partner, not just a data processor. For more targeted practice, you can explore additional interview insights and mock questions on Dataford. Approach your interviews with confidence, clarity, and a strong user focus, and you will be well-positioned to land the offer.