Axtria - Ingenious Insights for the Data Analyst Position

Axtria is a global provider of cloud software and data analytics to the Life Sciences industry, aiming to drive sales growth and improve healthcare outcomes for patients. Established in 2010, Axtria leads in deploying Artificial Intelligence and Machine Learning to stay ahead of the competition. With customers in over 75 countries, Axtria is recognized for its technological innovation and growth.

The Data Analyst position at Axtria involves working with SQL, Python, data science, and machine learning. The interview process typically includes an aptitude test, followed by technical and HR rounds. You will be evaluated on data analysis, SQL queries, and your knowledge of projects and internships.

If you're ready to make a meaningful impact, this guide will help prepare you for the interview process at Axtria. 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 Axtria as a data analyst. Whether you were contacted by an Axtria 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 Axtria 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 Axtria data analyst 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 Axtria data analyst role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Axtria’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst 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 Axtria 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 analyst role at Axtria.

Quick Tips For Axtria Data Analyst Interviews

• Review Core Technical Skills: Brush up on fundamental data science topics like SQL, Python, machine learning, econometrics, and linear programming. Basic questions on these topics often appear in the initial technical screening.
• Understand Axtria’s Platforms: Familiarize yourself with Axtria’s product offerings, such as DataMAx™, SalesIQ™, InsightsMAx™, and CustomerIQ™. Understanding how they leverage AI and machine learning can provide valuable context during interviews.
• Prepare for Behavioral Questions: Reflect on past experiences, particularly in collaborative settings. Axtria values team-oriented employees with strong problem-solving abilities, so be ready to showcase these traits.

Typically, interviews at Axtria vary by role and team, but commonly Data Analyst 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 datasets with student test scores, identify drawbacks of the current format and suggest changes for improved analysis. Discuss common issues in "messy" datasets.

• What metrics would you use to evaluate the value of marketing channels? For a company selling B2B analytics dashboards, determine which metrics are essential to assess the effectiveness and value of different marketing channels.

• How would you determine the next partner card for a company using customer spending data? Using customer spending data, outline the process to identify the most suitable partner for a new credit card offering.

• How would you investigate if a redesigned email campaign led to an increase in conversion rates? Analyze the impact of a redesigned email journey on conversion rates, considering other potential influencing factors. Determine if the observed increase is attributable to the new campaign.

• 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 value and next keys. 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, and how would you reformat them for better analysis? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in "messy" datasets.

• What is the expected churn rate in March for customers who bought a subscription since January 1st, given specific churn patterns? 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, calculate the expected churn rate in March for all customers who bought the product since January 1st.

• How would you explain a p-value to a non-technical person? Describe what a p-value is in simple terms for someone who is not familiar with technical or statistical concepts.

• What are Z and t-tests, and when should you use each? Explain what Z and t-tests are, their uses, the differences between them, and when to 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 and aggregates their results. 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 the scenarios where bagging and boosting algorithms are appropriate. Provide examples of the tradeoffs, such as bagging reducing variance and boosting improving accuracy but being more prone to overfitting.

• 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. Highlight how Lasso performs feature selection by shrinking coefficients to zero, while Ridge penalizes large coefficients without eliminating them.

• What are the key differences between classification models and regression models? Describe the fundamental differences between classification and regression models. Emphasize that classification models predict categorical outcomes, while regression models predict continuous outcomes. Discuss their respective use cases and evaluation metrics.

What is the average salary for a Data Analyst at Axtria - Ingenious Insights?

Q: What is the interview process for a Data Analyst position at Axtria? The interview process typically consists of three stages: an online assessment, a technical round, and an HR round. The online assessment includes logic, reasoning, data interpretation (LRDI), verbal aptitude (VA), and arithmetic aptitude questions. The technical round focuses on data science, SQL, Python, and project-related questions. The HR round covers company-related questions and your interest in the Data Analyst role.

Q: What are some common technical questions asked during the interview? Common technical questions include SQL queries and concepts, basic data science questions, machine learning, econometrics, and linear programming problems. You might also be asked about programming languages such as Python and R, as well as questions related to data mining and data modeling.

Q: How can I prepare for the online assessment and technical interview? To prepare, practice logical reasoning, data interpretation, and basic arithmetic aptitude questions. For the technical interview, review SQL, Python, machine learning, and data science concepts. Completing projects and using Interview Query to practice these concepts can be extremely beneficial.

Q: What is the company culture like at Axtria? Axtria boasts a transparent and collaborative culture, focusing on continuous learning and development. The Axtria Institute provides industry-leading training, and a customized career progression ensures associates' success in a fun, supportive environment. The company encourages innovation and teamwork among its diverse, talented workforce.

Q: Why should I consider working at Axtria? Axtria is a leading global provider of cloud software and data analytics specifically for the Life Sciences industry. By joining Axtria, you will work on impactful projects that improve healthcare outcomes, collaborate with industry experts, and advance your career through continuous learning and growth opportunities.

Conclusion

Preparing for a Data Analyst position at Axtria can be a fulfilling yet challenging adventure. A blend of technical expertise in SQL, Python, and data science, along with soft skills like communication and problem-solving, is crucial to excel in the interview process. The journey typically includes multiple rounds focusing on aptitude, technical skills, and HR evaluations, enabling candidates to demonstrate their strengths comprehensively.

If you want more insights about the company, check out our main Axtria 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 Axtria’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 Axtria data analyst 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!