Preparing For The Business Side Of Technical Interviews

The best-paying technical roles require extensive business acumen and communications skills. No one tells you this, but most candidate rejections and interview failures have nothing to do with the technical questions. That’s why you’ll often hear people say, “I aced the technical interview but never heard back from them.” A business question came up somewhere in the process, and the candidate didn’t handle it well.

In this article, I help you identify a business question by providing examples and deliver tips on preparing to answer it successfully. I will write a follow-up with sample answers if there's enough interest.

I put several different approaches to research in each section. You don’t need to do all of them, and it will rarely be possible to due to information and access limitations. I provide several options because only one or two may end up being feasible. Get the most complete picture possible, but don’t worry about building a perfect one.

Practice Questions

How do you increase adoption rates for your data products? Think in terms of internal users and paying customers.

How do you measure data quality's ROI? Replacing data quality with infrastructure is common.

How do you keep initiatives customer or user-focused during the development process?

Describe a time when you quantified the data team's business impact to senior leaders to justify or avoid losing budget for an initiative.

How do you connect data and AI initiatives to the business’s strategy or goals? (Or for a less senior role) How do you align business metrics with model metrics?

What business barriers, culture or strategic, have you faced and discuss a time when you overcame them?

How would you go about selling and influencing CxOs to get buy-in for data science initiatives? Discuss a time when you successfully worked with C-level or executive leaders.

How do you identify AI use cases and opportunities? Where are the biggest near-term opportunities for AI, and which popular opportunities should be avoided?

How can data teams build a track record of success to gain business leaders’ trust?

What does monetization mean, and how do you monetize data and AI?

What does a product-first approach mean to you? Discuss the pros and cons.

How do you assess the cost of curating a training data set for a model-supported product or large feature?

How do you convince a CEO to make a different decision (change their mind, change strategy, or do something different) using data and models?

What questions would you ask business leaders to identify their data needs?

How do you evaluate a new opportunity with data vs. expert opinions?

How do you reframe a business question as a data science question?

How do you approach answering business questions with data and models?

How do you balance business needs with the complexity of delivering a model-supported feature, particularly in early-maturity businesses?

How do you determine which business questions have value, or how do you select analytics initiatives based on a business need?

How would you explain the importance of prompt engineering to a non-technical CxO?

Describe a situation where you had to justify the returns of a model-based solution versus a solution leveraging a traditional approach. How did you frame the value for stakeholders?

A CEO asks, "How much new revenue is the data team generating this year, and how much will you save us?" How would you approach quantifying the data team's impact in a way that resonates with the CEO?

Describe an experience translating technical language into business language for C-level and executive leaders. Can you give an example of when it was critical?

How do you balance value delivery, cost, and delivery time when considering Generative AI solutions versus simpler, lower-cost technical alternatives?

How would you explain to a technical team member why it is important to define the problem space before implementing technical solutions?

How do you handle a situation where a business or leader wants to implement a technically feasible AI solution that doesn't align with its strategic goals or offer a clear path to monetization?

How To Research Companies

To prepare for business acumen questions in a job interview, we should research the company’s market position, challenges, and opportunities. This involves understanding the company's business model and strategic goals and how it uses data and AI to achieve them.

Analyze Job Descriptions and Employee Profiles: Look at job descriptions of the company and employee profiles on LinkedIn to understand the company's maturity journey, organizational structure, and how employees define their roles. See if roles are defined beyond the technology stack. Check if the job description aligns with how people explain their roles on LinkedIn.

Identify Business Problems and Opportunities: Research the business problems or opportunities the company is trying to address. Understand the organization's needs, priorities, industry trends, and competitive factors.

Competitive Analysis: Conduct a competitive analysis to understand where the business stands in relation to its primary competitors.

Review Earnings Reports and Financial Data: Read recent earnings reports to understand changing spending habits. Assess how the company connects projects and decisions to shareholder value.

Examine Data Maturity: Determine the company's data maturity by evaluating how they use data points, provide context, and dig deep into the "why" behind trends. Search for their C-suite’s public appearances and listen for opinion-driven vs. data-driven thinking.

Research Company's AI and Data Strategy: Understand the company's AI and data strategy. Determine if the company has a forward-looking approach to defining data and AI's place in the business. Most businesses will have something on their website or have mentioned it during earnings calls.

Reverse Engineer: If they don’t, infer the company's data and AI strategy by reverse engineering from its products. What are the most recent data and AI-enabled features the company has released? How are the features explained? Look for use cases or case studies on the company’s website if it’s a B2B company.

Talk to Employees and Industry Experts: If you have access through your network or an opportunity through another channel, talk to people from different organizations, learn about their jobs, and ask about their biggest challenges and expectations for technology and data.

Review External Communications: Look for mentions of the company's AI initiatives in the media. Analyze what the company's competitors and partners are doing in response.

How To Research Products

To effectively answer questions related to a company's products, we should research and understand the products' strategic positioning, value proposition, and market impact. While researching the business, you should come across some of this.

Understand the Product's Purpose: What is the problem the product solves and the needs it fulfills for customers? This can be achieved by reviewing product descriptions, customer testimonials, and use case documentation.

Analyze the Product's Market Fit: Evaluate how well the product aligns with market needs and customer workflows. Assess the product's competitive advantages and differentiators, which can be identified through competitive analysis and market research reports.

Investigate the Monetization Strategy: Examine how the company generates revenue from the product, including pricing models, monetization frameworks, and go-to-market strategies. This can be gleaned from company announcements, financial reports, and industry publications.

Determine the Data and AI Integration: Assess how the company leverages data, analytics, machine learning, and AI to enhance product functionality, personalization, and customer experience. This includes understanding the types of AI technologies used and how/if they contribute to the product's value proposition.

Analyze Product Roadmaps and Future Directions: Look for information about the company's product roadmap, planned features, and long-term vision. This provides insights into the company's strategic priorities and how the product is expected to evolve.

How To Research The Marketplace

We should research the company's marketplace by analyzing industry trends, the competitive landscape, and customer needs. Understanding the market dynamics, the company's position, and the factors driving customer behavior is key.

Industry Analysis: Examine industry reports, market research, and news articles to understand the overall trends, growth drivers, and challenges in the company's industry. This includes identifying key performance indicators (KPIs) and metrics used to evaluate success in that industry.

Competitive Landscape: Identify the company's main competitors and analyze their strengths, weaknesses, strategies, and market positioning. Job descriptions can be a good indicator of where each competitor is in their data maturity journey. This analysis can reveal opportunities for the company to differentiate itself and gain a competitive advantage. Think about how your role can support these.

Customer Analysis: Understand the company's target customers, their needs, preferences, and pain points. Review customer testimonials, case studies, and online reviews to gain insights into customer satisfaction and areas for improvement. If possible, determine how the company segments its customers and tailors its products and services to meet their specific needs.

Value Creation and Return: Research how the company creates and returns value within its marketplace. This involves understanding the company's value stream, workflows, and how they connect to business decisions.

Marketplace Position: Determine where the business is in relation to its primary competitors by performing a traditional competitive analysis. Employee profiles on LinkedIn are also good sources of information.

Technology and Innovation: Evaluate the role of technology, particularly data and AI, in the company's marketplace. If possible, understand how the company uses data to gain insights into customer behavior, optimize operations, and develop new products and services. Determine whether the company has a forward-looking approach to defining data and AI's place in the business.

How To Research The Domain & How Deep To Go

In some cases, it’s only feasible to research the business domain because there’s not much publicly available information on the business. Focus on understanding the industry's dynamics, key players, and value drivers. The goal is to grasp the intricacies of the domain so you can discuss challenges and opportunities with a nuanced perspective.

Select the company’s primary domain and start by reviewing surveys by Accenture, IBM, etc. on AI maturity and applications.

Set up a Google Alert for the domain + "machine learning" or “AI.” You’ll get daily updates with posts containing keywords and language senior leaders use. Create a second layer of Google Alerts using key terms from the initial alerts to get more granular content. Reading 1 to 2 posts daily for just a couple of weeks will provide a solid conceptual understanding and the ability to speak to major themes and needs.

Follow software vendors that support the domain. Identify and bookmark major software vendors and check their websites weekly for blog posts, product updates, and features. Use Gartner and Big 5 consulting companies as resources for domain-specific content as well. Search for the domain on arXiv and Papers with Code. Reading abstracts and conclusions of 1-3 papers per week helps focus on common technical approaches and open challenges.

Focus on workflows and the expertise required to complete them and deliver high-quality work products. Understand the tasks and decisions involved in those tasks.

If possible, attend non-technical industry conferences to network and learn about significant challenges and opportunities. Look for workshops and seminars that include hands-on learning.

It may not sound like much, but reading articles, surveys, and research might be all you have access to. Depth would be nice, but being able to discuss a few nuanced points about the business or even the domain is enough to make you stand out.

When you’re ready to hone your product, strategy, and value-centric capabilities, check out my courses.