Flexshopper is a publicly traded company renowned for its consumer-centric approach and innovative marketing strategies. As of March 2023, Flexshopper continues to expand its market presence by leveraging the power of data to drive consumer insights and enhance decision-making processes.

Joining Flexshopper as a Data Scientist within the marketing group involves playing a crucial role in shaping the company's data-driven initiatives. This position requires strong expertise in Python, data manipulation, and data visualization. With a minimum of 5 years of experience, especially in consumer businesses, you'll analyze complex datasets to uncover trends and support strategic marketing decisions. While preference is given to onsite candidates, remote flexibility is available for the right individuals.

This guide on Interview Query will provide you with the insights and preparation tips you need to ace your interview for this exciting opportunity at Flexshopper. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Flexshopper as a Data Scientist. Whether you were contacted by a Flexshopper recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences, particularly focusing on your proficiency in Python, data manipulation, and data visualization, as well as any experience in consumer businesses.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Flexshopper Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Flexshopper Data Scientist hiring manager may be present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Flexshopper Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Flexshopper’s data systems, ETL pipelines, Python coding, data manipulation, and data visualization.

In the case of Data Scientist roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Flexshopper office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Scientist role at Flexshopper.

Quick Tips For Flexshopper Data Scientist Interviews

• Master Python and Data Visualization: Ensure your Python skills are top-notch, as these are crucial for the role. Be proficient in data manipulation and visualization, as these will be core areas of focus during the interview.
• Understand Consumer Business Analytics: Flexshopper values experience in consumer businesses. Highlight any relevant experience and be prepared to discuss how you've previously handled data insights within a consumer-focused setting.
• Practice Behavioral Questions: Flexshopper's culture emphasizes teamwork and problem-solving. Practice responding to behavioral questions that showcase your ability to collaborate effectively and solve complex problems within a data-driven framework.

Typically, interviews at Flexshopper vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

• How would you reformat student test score data for better analysis? Given two datasets of student test scores, identify drawbacks in their current organization. Suggest formatting changes and discuss common issues in "messy" datasets.

• What metrics would you use to evaluate the value of marketing channels? Given data on marketing channels and costs for a B2B analytics dashboard company, identify key metrics to determine the value of each marketing channel.

• How would you determine the next partner card for a company? With access to customer spending data, describe the process to identify the best partner for a new credit card offering.

• How would you investigate the impact of a redesigned email campaign on conversion rates? Analyze the increase in new-user to customer conversion rates after a redesigned email journey. Determine if the increase is due to the campaign or other factors.

• Write a function search_list to check if a target value is in a linked list. Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with keys value and next. If the linked list is empty, you'll receive None.

• Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product. Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

• Create a function digit_accumulator to sum every digit in a string representing a floating-point number. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

• Develop a function to parse the most frequent words used in poems. You're hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

• Write a function rectangle_overlap to determine if two rectangles overlap. You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

• How would you design a function to detect anomalies in univariate and bivariate datasets? If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

• What are the drawbacks of the given student test score data layouts? Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

• What is the expected churn rate in March for customers who bought the product since January 1st? You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers since January 1st?

• How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• What are Z and t-tests, and when should you use each? What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

• How does random forest generate the forest and why use it over logistic regression? Explain the process of how random forest generates multiple decision trees to form a forest. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms and describe scenarios where bagging is preferred over boosting. Provide examples of the tradeoffs, such as variance reduction in bagging and bias reduction in boosting.

• How would you evaluate and compare two credit risk models for personal loans?

• Identify the type of model developed by the co-worker for loan approval.
• Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.

• List the metrics to track for measuring the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

• What’s the difference between Lasso and Ridge Regression? Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

What is the average salary for a Data Scientist at Flexshopper?

Q: What does the Data Scientist position at FlexShopper entail?

A: The Data Scientist role at FlexShopper focuses on supporting our marketing group. Key responsibilities include data manipulation, data visualization, and utilizing Python to extract insights. You will work to optimize marketing strategies rooted in data-driven decisions.

Q: Is the Data Scientist position at FlexShopper remote or onsite? A: Our preference is to have the position onsite, but remote work may be considered for selected candidates who demonstrate outstanding qualifications and fit.

Q: What specific skills and experience does FlexShopper look for in a Data Scientist? A: We prioritize candidates with over 5 years of experience in Python, data manipulation, and data visualization. A background in consumer businesses is highly advantageous. Strong analytical skills and the ability to transform data into actionable insights are essential.

Q: What type of company is FlexShopper? A: FlexShopper is a public company specializing in consumer financing and lease-to-own solutions. We focus on making essential products more accessible to consumers through flexible payment options.

Q: How can I best prepare for an interview with FlexShopper? A: To prepare for an interview, research our company and market positioning. Brush up on common data science interview questions, specifically in Python, data manipulation, and visualization. Platforms like Interview Query can be instrumental in your preparation.

Looking for an exciting opportunity to leverage your expertise in Python, data manipulation, and data visualization? Flexshopper, a leading public company in the consumer business sector, seeks talented data scientists/engineers for their marketing group. While we prefer onsite candidates, remote options are available for the right fit. If you've got 5+ years of experience, don't miss your chance to make an impact here!

For detailed insights on the interview process, check out our Flexshopper Interview Guide, where we've compiled key interview questions just for you. Discover tailored interview guides for other roles at Interview Query and equip yourself with the strategies to excel in your interview.

Good luck with your Flexshopper interview!