At InMoment, we pride ourselves on our mission of Experience Improvement (XI), helping clients enhance experiences at the intersection of customer, employee, and business needs. As a leading player in customer and employee experience, we leverage innovative technology and expert insights to challenge the status quo across industries.

We are currently seeking a passionate Data Scientist to help tackle complex, data-driven questions. As a Data Scientist at InMoment, you’ll lead the design and statistical analysis of experimental data, collaborating with marketing science experts to define our future approach. This role is ideal for someone with advanced statistical programming skills and a knack for solving intricate problems.

If you're ready to join a team that values making impactful, deliberate decisions to improve lives, our detailed interview guide on Interview Query will prepare you for the journey ahead. Let's get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining InMoment as a data scientist. Whether you were contacted by a 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.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the InMoment 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 InMoment data scientist hiring manager stays 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 InMoment 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 InMoment’s data systems, ETL pipelines, and statistical analysis.

In the case of data scientist roles, take-home assignments regarding experimental design, sampling methodologies, and advanced statistical analysis are incorporated. Apart from these, your proficiency against statistical programming skills, particularly in R or SAS, 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 InMoment office. Your technical prowess, including programming and 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 InMoment.

Quick Tips For InMoment Data Scientist Interviews

• Master Advanced Statistics and Programming: InMoment thrives on statistical analysis and experimental design. Brush up on your knowledge of advanced statistics and make sure you are proficient in statistical programming languages like R or SAS.
• Understand InMoment's Core Mission: InMoment focuses on Experience Improvement (XI) across various sectors. Be ready to discuss how your skills and expertise align with their mission and how you can contribute to their goals.
• Be Solutions-Oriented: Highlight your problem-solving skills and critical thinking during the interview. Be ready to provide real-life examples where you've used these skills to drive issues to resolution.

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

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: What's the time complexity?

• Create a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Develop a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you suspect anything unusual about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What steps would you take if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What actions would you take to investigate and address this issue?

• Why might job applications be decreasing while job postings remain constant? You observe that the number of job postings per day has remained stable, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common issues found in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• Write a function to calculate sample variance from a list of integers. Create a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. Example: Input: test_list = [6, 7, 3, 9, 10, 15] Output: get_variance(test_list) -> 13.89

• Is there anything suspicious about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

• How to find the median in a list with more than 50% of the same integer in O(1) time and space? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in (O(1)) computational time and space. Example: Input: li = [1,2,2] Output: median(li) -> 2

• What are the drawbacks and formatting changes needed for messy datasets? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes to make the data more useful for analysis, and describe common problems seen in messy datasets. Example datasets:

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model for this problem? If you proceed with the decision tree, how would you evaluate its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its forest. Additionally, discuss why one might choose random forest over other algorithms such as logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which scenarios would you use a bagging algorithm versus a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to track the accuracy and validity of the model?

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

Q: What is InMoment's mission and culture like? At InMoment, we are dedicated to our Experience Improvement (XI) mission, helping clients enhance experiences at the intersection of value—where customer, employee, and business needs converge. We challenge the status quo, drive innovation with our state-of-the-art technology, and support a unique culture that emphasizes owning the moments that matter. Our team is committed to making an impact in every interaction.

Q: What qualifications are needed for the Data Scientist position at InMoment? To qualify for this role, you should have a Ph.D. in a relevant field or extensive professional experience, a background in market research or statistically sophisticated fields, advanced statistical programming skills (especially in R or SAS), and enthusiasm for cutting-edge approaches like Bayesian Inference and Causal Modeling.

Q: What responsibilities does the Data Scientist role entail? As a Data Scientist at InMoment, you will consult on study design and analysis, including experimental design, sampling methodologies, and advanced statistical analyses. You'll collaborate with internal and external clients, scope projects, refine processes, and stay on the cutting edge of your field by identifying innovative problem-solving techniques.

Q: What benefits does InMoment offer to its employees? InMoment provides a variety of benefits, including autonomy with a flexible work schedule, unlimited PTO, comprehensive medical, dental, and vision insurance, a 401(k) plan with a generous company match, parental leave, access to wellbeing initiatives, and a supportive, collaborative work environment. We also have inclusion and diversity teams that promote different perspectives and backgrounds.

Q: How can I prepare for an interview at InMoment? To prepare, research our company and familiarize yourself with our mission and values. Understand the key responsibilities and required skills for the Data Scientist position. Practicing common data science interview questions on platforms like Interview Query can also be very helpful. Be ready to discuss your statistical programming expertise and experience with market research or experimental fields.

Ready to take the next step?

Join a team at InMoment where your expertise in data science will truly matter! As a Data Scientist, your skills will be at the forefront of solving some of the most challenging and intriguing problems, leading transformative marketing sciences for top-tier customers. At InMoment, we don't just talk about innovation, we live it—evolving the standards of customer, employee, market, and product experiences. If you're passionate about pushing the boundaries with advanced statistics and cutting-edge approaches, InMoment is the place to make your mark.

If you want more insights about the company, check out our main InMoment Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about InMoment’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every InMoment interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!