Welcome to Veradigm! Our mission is to be the most trusted provider of innovative solutions empowering all stakeholders across the healthcare continuum for world-class outcomes. With a vision of creating a Connected Community of Health globally, Veradigm holds the largest client community in healthcare.

As a Principal Data Scientist, you will play a pivotal role in leading the development and evaluation of analytic solutions for Veradigm Payer markets. You will design predictive models, partner with executive teams to translate business needs into analytic requirements, and oversee the migration to an Azure-based environment to enhance analytics' scope and performance.

At Veradigm, we believe in empowering our associates with tools and flexibility for professional growth. Dive deeper into our culture, benefits, and career opportunities to see how you can contribute to a connected health community.

Submitting Your Application

Welcome to Veradigm! Our mission at Veradigm is to be the most trusted provider of innovative solutions that empower all stakeholders across the healthcare continuum to deliver world-class outcomes. Our vision is a Connected Community of Health that spans continents and borders.

To get started, submit a compelling application that highlights your technical skills and your passion for joining Veradigm as a Data Scientist. Review the job description carefully and tailor your CV to include keywords and skills that the hiring manager might be looking for. Additionally, craft a targeted cover letter that emphasizes your relevant experiences and unique contributions you can bring to Veradigm.

Recruiter/Hiring Manager Call Screening

If your application is shortlisted, a recruiter from Veradigm's Talent Acquisition Team will reach out to verify key details about your experience and skills. This initial call might also include some behavioral questions to assess your fit within the company culture.

In some cases, the Veradigm Data Scientist hiring manager may be present to answer any questions you have about the role and the company. You might discuss your technical skills briefly, but this phase is mainly to ensure alignment of your profile with the company’s needs.

The recruiter call typically lasts around 30 minutes.

Technical Virtual Interview

Successfully passing the recruiter round will earn you an invitation to the technical screening phase. Conducted virtually through video conferencing, this 1-hour interview will test your understanding of data systems, ETL pipelines, and SQL queries. For the Veradigm Data Scientist role, you might also face questions on statistical techniques, machine learning, and predictive modeling relevant to the healthcare sector.

Be prepared for tasks involving product metrics, analytics, and data visualization, as well as questions on hypothesis testing, probability distributions, and more advanced machine learning concepts.

Depending on the position's seniority, you might also be given real-world data problems or case studies that you'll need to solve.

Onsite Interview Rounds

After passing the technical virtual interview, you’ll be invited for onsite interviews at Veradigm. This stage typically involves multiple interview rounds, focusing on various aspects of your technical and analytical prowess. Expect in-depth discussions about your programming skills, machine learning models, and how you would apply these to solve Veradigm-specific healthcare challenges.

If you were given a take-home assignment, you might need to present your findings and solutions during this onsite interview.

Quick Tips For Veradigm Data Scientist Interviews

Here are some tips to help you prepare for your interview at Veradigm:

• Master Healthcare Data: Given Veradigm's extensive work in the healthcare industry, be prepared to demonstrate your capability in handling complex, large-scale healthcare datasets.
• Showcase Predictive Analytics Skills: Highlight your experience and knowledge in predictive modeling and machine learning, especially as they pertain to healthcare applications such as risk scoring and clinical data analysis.
• Understand Business Needs: Be ready to discuss how your technical skills can relate to and support Veradigm’s business and product requirements. Clear communication is crucial, as you'll often need to translate your analytical findings into actionable business strategies.

Typically, interviews at Veradigm vary by role and team, but commonly Data Scientist 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 whether 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 dishonest users, such as those who drive a car while claiming to be on a bike ride. Specify the metrics and statistical methods you would analyze.

• 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 and log-likelihood as the loss function. Create a logistic regression model from scratch without an intercept term. Use basic gradient descent (with Newton's method) for optimization and the log-likelihood as the loss function. Do not include 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 .04 p-value? Your company is running a standard control and variant AB test to increase conversion rates on the landing page. The PM finds a p-value of .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?

• Should Square hire a customer success manager or offer a free trial for a new product? Square's CEO wants to hire a customer success manager for a new software product, while another executive suggests offering a free trial instead. What would be your recommendation to get 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 Scientist at Veradigm?

Q: What is Veradigm's mission and vision?

A: Veradigm's mission is to be the most trusted provider of innovative solutions that empower stakeholders across the healthcare continuum to deliver world-class outcomes. Our vision is a Connected Community of Health that spans continents and borders.

Q: What does the Principal Data Scientist role entail?

A: The Principal Data Scientist leads the development and evaluation of best-in-class analytics for Veradigm Payer markets. Responsibilities include designing and building models of healthcare behavior, partnering with the Chief Data Scientist and executive team, and overseeing the migration to an Azure-based environment for enhanced analytics.

Q: What skills are essential for the Principal Data Scientist position at Veradigm?

A: Key skills include extensive experience in the healthcare industry, proficiency with statistical techniques and machine learning models, strong data management capabilities using SQL, Databricks, or Snowflake, and excellent communication and leadership abilities. Experience with MA, ACA, or Medicaid Risk Scoring is also beneficial.

Q: What kind of work environment can I expect at Veradigm?

A: Veradigm believes in empowering associates with the tools and flexibility needed for professional development. The company emphasizes a collaborative work culture, offers remote work opportunities, and promotes diversity and inclusion within the workforce.

Q: How can I prepare for the interview for the Principal Data Scientist position at Veradigm?

A: To prepare, familiarize yourself with Veradigm's mission, and practice technical and behavioral interview questions using resources like Interview Query. Demonstrating an understanding of healthcare analytics and your ability to connect business needs with analytic approaches is crucial.

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