Behavioral Questions Flashcards
Tell me about yourself
- I’m a business and data analyst and that’s been my professional focus for the past 5 years.
- I actually came to this line of work in a slightly non-tradition way. I started out as full stack software developer: introduced to relational databases, SQL and the SDLC as well as requirements gathering, functional specs and writing documentation.
- Later I move into management: Less hands on, more extensive customer communication, presenting, analyzing needs, transitioning business goals into technical solutions.
- I really enjoyed the time I spent on those roles, but about 5 years ago when I decided to pivot my career away from people management positions, I chose to focus on business and data analytics because I felt like it was a great opportunity to combine my skills in communication, business analysis, problem solving with technical background.
- That’s what’s lead me here and business and data analytics is what I’ve been focusing on in my last 3 positions, including most resent role as a data analyst at Harmony Data and Analytics where my role involves working closely with the client to understand and document their business requirements, what data they need and how they want to consume it, as well as being the bridge to the technical team making sure implementation stays in line with expectations.
I’m currently looking for a new team where I can continue to contribute and grow in those areas of specialization.
Tell me about your most recent position
Currently I am working on a consulting assignment as a data analyst for Harmony Data and Analytics where our client is looking to start marketing a new line of products and services.
The focus of my role is working with the client to get a comprehensive knowledge of their business needs, understanding of their product and goals of the initiative. Since this was a new initiative for the client, we worked with them to identify what the KPI’s are and how they could use data to help track them as well as the other benchmarks for success
I am also responsible for documenting the requirements and ensuring signoff from all the stakeholders.
From there my role involves working with other technologist to create functional specifications and, because this was such a small team, helping to implement the data structure, ETL process, and visualizations that would be used to provide the client insight while continuing to be the primary liaison with the client throughout the project.
Describe a time when you had to present complex data to non-technical individuals? How did you simplify the information?
In my previous role as a data business analyst at Brooklyn Properties, I was tasked with presenting the results of a systems and process analysis to the company’s CEO who was one of the least technical people I have worked with.
The goal was to convey intricate insights about current system and identify areas for process improvements and ways to make data more accessible for business decisions marketing strategies.
To make the information more accessible, I took the following steps:
Visualizations: I created visually engaging charts and graphs to represent the key findings and convey relationships and trends without overwhelming the non-technical stakeholders.
Storytelling Approach: Instead of diving into technical details, I adopted a storytelling approach. I framed the data in a narrative that highlighted the key takeaways, starting with a relatable context. For example, I began by explaining the different customer segments as distinct ‘personas’ with specific characteristics and behaviors.
Plain Language: I consciously avoided technical jargon and used plain language to describe complex concepts. For instance, instead of discussing the algorithm, I focused on the practical implications, such as how certain customer segments were more likely to respond to specific marketing strategies.
Question Anticipation: Knowing that non-technical individuals might have questions, I anticipated potential queries and prepared clear, concise responses. This proactive approach helped in addressing concerns and facilitating a smoother understanding of the presented data.
As a result of these strategies, he not only grasped the nuances of the customer segmentation analysis but also felt empowered to make informed decisions based on the insights.
How do you stay updated with the latest tools and techniques in data analysis?
- Online Courses and Platforms: Udemy
- Pursue certifications: Tableau Data Analyst
- Connect with professionals and stay abreast of forums and conversations (LinkedIn)
- Social group: Drink and learn with colleagues to discuss challenges and trends
Specifically why do you want to work for our company?
Pantheon
My past few positions have been with smaller companies, looking for a medium sized company with some additional opportunities to work on more complex issues to continue to learn and grow as a data and business analyst. I love Pantheon’s commitment to providing attention to detail while still finding ways to be agile and move quickly. Resonates with me….finding ways to move quickly and stay flexible while still delivering high quality. It’s also nice that you’re fairly close to where I love. Love working remotely, but it’s nice to be able to occasionally connect in person as well. Based on JD and conversations, seems like my skills would be a good fit for the position, i would be able to make some important contributions to the team and it would an environment that I would be excited to work in.
What questions do you have for me?
Company Specific
1a.
1b.
- . How would you describe the culture at [company]?
- What are some of the most important traits that a successful candidate in this role will possess?
- If I were to move forward, what does the rest of your interview and decision making process look like?
- Who would I be reporting to?
- How would I be evaluated or what are the benchmarks for success in this role?
- Do you have any other questions for me or concerns about my ability to fill this role that I can address?
Tell me about a situation when you used data to tell a compelling story that led to a business decision.
What is logistic regression?
Logistic regression is a data analysis technique that uses mathematics to find the relationships between two data factors. It then uses this relationship to predict the value of one of those factors based on the other. The prediction usually has a finite number of outcomes, like yes or no.
How do you determine which statistical methods to use when analyzing data?
Selection of appropriate statistical method depends on the following three things: Aim and objective of the study, Type and distribution of the data used, and Nature of the observations (paired/unpaired).
Explain the difference between data profiling and data mining.
Data profiling is typically considered to be more of a data validation process where data is summarized and analyzed in order to find errors, missing information, and inconsistencies.
Data mining is the process of performing a more extensive evaluation of the data in order to answer business questions, uncover trends and patterns, and make predictions.
What are some of the common challenges you encounter as a data analytics professional?
- having access to complete, consistent, and accurate data,
- getting clear buy-in from all stakeholders regarding the desired objective of the study,
- managing user expectations when the data doesn’t produce the answers or predict the trends they were hoping for.
- data infrastructure or security limitations
Tell me about a time when you had to handle multiple data analysis projects simultaneously
AND/OR
had a really tight deadline.
AND/OR
had to balance several high priority projects. How did you manage your time and ensure on time delivery?
Tell me about a time when you had to collaborate with someone with a different personality style than yours
AND/OR
didn’t get along with a team member
AND/OR
Disagreed with your decision/point of view?
AND/OR
Had a difficult customer. How did you handle it?
Describe a time when your attention to detail prevented a mistake in data analysis.
AND/OR
Share an example where you identified a discrepancy in data that others missed? What was the impact?
In my previous role as a data analyst at Baker Wee, I was tasked with analyzing sales and profit trends and provide insights for an upcoming sales promotions.
As part of the analysis, I was responsible for cross-referencing data from different sources including the physical POS system, online sales receipts, and accounting software.
While working on this task, I noticed a discrepancy in the pricing data provided by the different sources. The numbers weren’t aligning, and upon closer inspection, I discovered an error in the data extraction process.
I traced back the data to its source, identified the issue in the extraction script, and rectified the error. As a safety measure, I implemented a validate process in the script to catch such discrepancies early in future analyses.
As a result of my attention to detail, we were able to correct the error before it affected the project’s strategic decisions.
How do you ensure accuracy when handling large datasets?
Start with Data Profiling: Making sure to understand the structure, patterns, and quality of the data. This helps Identify missing values, outliers, and potential issues early in the analysis.
Having good Data Cleaning and Preprocessing procedures to handle missing values, outliers, and inconsistencies based on business rules. Also work to standardize and normalize data to ensure consistency and comparability.
Data Validation:
- Implement data validation checks to identify errors or discrepancies in the dataset.
- Cross-verify data with external sources or against business rules to validate its accuracy,
- Use automated tools and QA scripts to perform routine checks and identify anomalies,
- Use sampling techniques to assess the quality (Randomly select and analyze subsets of the data to validate assumptions and results.)
