UT Health Houston is a leading medical and research institution dedicated to improving the health and well-being of individuals. Known for its cutting-edge research and commitment to patient care, UT Health Houston offers a dynamic and supportive environment for professionals.

As a Data Scientist at UT Health Houston, you will play a crucial role in leveraging data analytics to drive insights and support medical research. This position requires strong analytical skills, proficiency in programming, and the ability to communicate effectively. The interview process typically begins with a phone screen by a recruiter or HR personnel, followed by an in-person interview with the hiring manager, and concludes with a presentation to a panel.

If you are aiming to advance your career in data science within the healthcare industry, this guide by Interview Query will walk you through the interview process, commonly asked questions, and valuable insights to help you succeed.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining UT Health Houston as a data scientist. Whether you were contacted by a UT Health Houston 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 UT Health Houston 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 UT Health Houston 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 UT Health Houston 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 your knowledge of data systems, ETL pipelines, and SQL queries.

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 UT Health Houston office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated throughout these interviews.

A final step may include a short presentation to a panel of interviewers at UT Health Houston.

Quick Tips For UT Health Houston Data Scientist Interviews

• Express Your Thoughts Clearly: The interview process at UT Health Houston values clarity and coherence. Practice articulating your thoughts in a structured manner.
• Know Your Stuff: Brush up on fundamental data science concepts, especially in statistics, probability, and machine learning. Familiarize yourself with data systems and ETL processes relevant to healthcare applications.
• Prepare For Behavioral Questions: Be ready to discuss experiences where you handled difficult situations with co-workers. Showcase how you communicate and work collaboratively within teams.

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Typically, interviews at Ut Health Houston 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. 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 subscriptions 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 who bought the product 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 how random forest generates multiple decision trees and combines 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 use cases for bagging and boosting algorithms. Provide examples of 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 installments.

• List metrics to track 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 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 main differences between classification and regression models. Emphasize that classification models predict categorical outcomes, while regression models predict continuous values.

What is the average salary for a Data Scientist at Ut Health Houston?

Q: What is the interview process for the Data Scientist position at UT Health Houston like? The interview process at UT Health Houston begins with an initial phone screen by a recruiter or HR personnel. The second step involves an in-person interview with the hiring manager. The final step involves giving a short presentation to a panel.

Q: What are some common interview questions for the Data Scientist position at UT Health Houston? Common interview questions include: - Why should we hire you? - How would you reverse a linked list? - Tell me about yourself. - Describe a difficult situation you have encountered with a co-worker on a project and how you helped improve the situation.

Q: What skills are required to work as a Data Scientist at UT Health Houston? To be successful in the Data Scientist role at UT Health Houston, you should have strong analytical, technical, and problem-solving skills. Experience with data analysis, machine learning, and statistical methods is essential. Excellent communication skills are also important as they help in clearly expressing thoughts and findings.

Q: What should I expect in the final presentation during the interview process? In the final step of the interview process, you will be required to deliver a short presentation to a panel. This presentation is an opportunity to showcase your technical skills, problem-solving abilities, and how you articulate and present your work.

Q: How can I prepare for an interview at UT Health Houston? To prepare for an interview at UT Health Houston, research the company and its mission, practice common interview questions, and review your technical skills. It's also beneficial to practice coding and data science problems on Interview Query to sharpen your problem-solving skills.

If you're passionate about making a difference in healthcare through data science, UT Health Houston could be your next career destination. The interview process is designed to understand your thought process and problem-solving abilities, starting with a phone screen, followed by an in-person interview with the hiring manager, and culminating in a presentation to a panel. Questions like "Why should we hire you?," "How would you reverse a linked list?," and behavioral inquiries such as describing a difficult project situation are common.

For more insights about the company, check out our main UT Health Houston 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 UT Health Houston'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 UT Health Houston machine learning engineer 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!