Welcome to Veradigm, where our mission is transforming health, insightfully. Veradigm is dedicated to addressing contemporary healthcare challenges by leveraging research, analytics, and artificial intelligence (AI). Join us as a Data Analyst and contribute to creating scalable, data-driven solutions that bring significant value to healthcare stakeholders, including biopharma, health plans, healthcare providers, and patients.

As a Data Analyst at Veradigm, you'll be instrumental in developing requirements for new analytics and data products, generating customer financial projections, and conducting in-depth research on risk adjustment performance. This role requires extensive experience in the healthcare industry, proficiency in SQL and SAS, and a strong analytical mindset with excellent problem-solving skills. If you're ready to dive into a dynamic, collaborative environment where you can make a tangible impact on smarter care for millions, this guide by Interview Query is for you.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Veradigm as a Data Analyst. Whether you were contacted by a Veradigm 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 Veradigm 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 Veradigm 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 Veradigm 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 Veradigm’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 Veradigm 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 Veradigm.

Quick Tips For Veradigm Data Analyst Interviews

• Understand Veradigm’s Healthcare Focus: Veradigm is deeply integrated into the healthcare industry. Familiarize yourself with healthcare-related data systems, especially those involving risk adjustment and CMS files.

• Proficiency in SQL and Data Tools: Ensure you have strong skills in SQL and analytics tools such as SAS, Python, R, and Snowflake. Practical knowledge in these areas is often heavily tested.

• Communicate Clearly and Effectively: Being able to clearly articulate your analytical findings and methodologies will be crucial. Practice discussing complex ideas in a simple, understandable format.

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

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• How would you design a machine learning model to classify major health issues based on health features? You work as a machine learning engineer for a health insurance company. Design a model that classifies if an individual will undergo major health issues based on a set of health features.

• What metrics and statistical methods would you use to identify dishonest users in a sports app? You work for a company with a sports app that tracks running, jogging, and cycling data. Formulate a method to identify users who might be cheating, such as driving a car while claiming to be on a bike ride. Specify the metrics and statistical methods you would analyze to detect athletic anomalies.

• Develop a function str_map to determine if a one-to-one correspondence exists between characters of two strings at the same positions. Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

• Build a logistic regression model from scratch using gradient descent without an intercept term. Create a logistic regression model from scratch using basic gradient descent and the log-likelihood as the loss function. Do not include an intercept term or a penalty term. You may use numpy and pandas but not scikit-learn. Return the parameters of the regression.

• Why are job applications decreasing despite stable job postings? 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 would you do if friend requests on Facebook are down 10%? A product manager at Facebook informs you that friend requests have decreased by 10%. How would you address this issue?

• How would you assess the validity of an AB test result with a 0.04 p-value? Your company is running an AB test to increase conversion rates on a landing page. The PM reports a p-value of 0.04. How would you evaluate the validity of this result?

• How would you analyze the performance of a new LinkedIn feature without an AB test? LinkedIn has launched a feature allowing candidates to message hiring managers directly during the interview process. Due to engineering constraints, an AB test wasn't possible. How would you analyze the feature's performance?

• Customer success manager vs. free trial for Square's new software product? The CEO of Square's small business division wants to hire a customer success manager for a new software product, while another executive suggests a free trial. What would be your recommendation for getting new or existing customers to use the new product?

• How would you build a fraud detection model using a dataset of 600,000 credit card transactions? Imagine you work at a major credit card company and are given a dataset of 600,000 credit card transactions. Describe your approach to building a fraud detection model.

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• How would you tackle multicollinearity in multiple linear regression? Describe the methods you would use to address multicollinearity in a multiple linear regression model.

• How would you design a facial recognition system for employee clock-in and secure access? You work as an ML engineer for a large company that wants to implement a facial recognition system for employee clock-in, clock-out, and access to secure systems, including temporary contract consultants. How would you design this system?

• How would you handle data preparation for building a machine learning model using imbalanced data? Explain the steps you would take to prepare data for building a machine learning model when dealing with imbalanced data.

What is the average salary for a Data Analyst at Veradigm?

Q: What does Veradigm specialize in?

Veradigm focuses on transforming healthcare by utilizing research, analytics, and artificial intelligence (AI) to develop scalable data-driven solutions. They cater to biopharma, health plans, healthcare providers, health technology partners, and patients to solve modern healthcare challenges and enable smarter care for millions of people.

Q: What are the key responsibilities of a Data Analyst at Veradigm?

A Data Analyst at Veradigm is responsible for developing business cases for new products, leading customer requests for reporting and research, performing financial improvement modeling, and collaborating with clinicians on outcomes and algorithm performance. They also provide training on the risk adjustment process and keep up to date with CMS regulatory changes.

Q: What qualifications are required for a Data Analyst position at Veradigm?

Applicants should have a Bachelor's degree in a relevant field (such as Actuarial Science, Math, or Statistics) and 5-7 years of experience with Medicare Advantage or ACA Risk Adjustment data. They should be proficient in SAS, SQL, and have experience with healthcare data sets, financial modeling, and clinical classification systems.

Q: What is the company culture like at Veradigm?

Veradigm believes in empowering its associates with the tools and flexibility to be their best at work and further their professional development. The company values diverse perspectives and emphasizes collaboration with healthcare providers, life science companies, and patients.

Q: How can I prepare for an interview at Veradigm?

To prepare for an interview at Veradigm, you should familiarize yourself with the company's mission and the specific responsibilities of the Data Analyst role. Practicing common interview questions and reviewing your technical skills using Interview Query can also be beneficial.

If you're excited about joining a company at the forefront of transforming healthcare through research, analytics, and artificial intelligence, Veradigm could be your perfect match. To dive deeper into Veradigm's interview process and prepare effectively, explore our comprehensive Veradigm Interview Guide on Interview Query. Here, you'll find a wealth of resources, including potential interview questions and insights tailored for roles such as a data analyst. Interview Query is your go-to platform for enhancing your interview skills with its extensive toolkits and strategic guidance.

Check out all our company interview guides for more preparation tips and insights. If you have any questions, feel free to reach out to us.

Good luck with your Veradigm interview!