Freenome is a dynamic biotech company committed to advancing cancer detection using sophisticated blood tests. Their multiomics platform integrates tumor signals with machine learning, aiming to identify cancer at its early, treatable stages. Since its inception in 2014, Freenome has raised over $1.1 billion in funding and continues to grow, bolstered by its mission-driven culture.

As a Research Scientist at Freenome, you’ll be at the forefront of groundbreaking research to improve diagnostic tests and patient outcomes. Your responsibilities will involve developing assays, analyzing biological markers, and collaborating with cross-functional teams to enhance test performance. This role offers a unique opportunity to leverage your expertise in blood cell biology for impactful, clinical applications within an innovative, collaborative environment.

Through Interview Query, we’ll guide you through Freenome’s interview process, highlighting key expectations and insightful tips to help you secure this transformative role.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Freenome as a Research Scientist. Whether you were contacted by a Freenome 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 Freenome 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 Freenome 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 Phone Interview

If you pass the initial recruiter screening, you will be scheduled for a longer 1-hour phone interview with more technical questions. This round will focus on your relevant experiences and technical knowledge. Expect questions about the pros and cons of different biological techniques and your practical experiences around these methods.

Onsite Interview Rounds

After successfully navigating through the technical phone interview, you will be invited for onsite interviews. Multiple interview rounds, varying with the role, will be conducted at the Freenome office. These interviews will include detailed technical evaluations, where you will demonstrate your expertise, including designing and prototyping blood cell-based sample QC assays.

You may also need to present any relevant projects or case studies you have worked on. Collaboration and communication skills are key points of assessment in these onsite interviews.

Final Decision

Upon successfully completing the onsite interviews, the hiring committee will review your performance across all stages. If you are selected, a recruiter will get in touch with you to extend an offer including details about compensation and benefits.

Quick Tips For Freenome Research Scientist Interviews

• Know the Fundamentals: Be well-versed with the basics and advanced aspects of blood cell biology, diagnostics, and multiomics platforms integrating machine learning.
• Hands-on Experience: Freenome highly values practical experience. Make sure you’re comfortable discussing your hands-on experience with relevant techniques and technologies such as NGS, blood cell stabilization, and QC assays.
• Professional Communication: Effectively communicate your scientific problem-solving abilities and how they can contribute to Freenome's mission of early cancer detection.

Click here to start your journey with Interview Query

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

• How would you explain what a p-value is to someone who is not technical? Explain the concept of a p-value in simple terms to someone without a technical background. Focus on its role in hypothesis testing and what it indicates about the results.

• How many more samples would we need to decrease the margin of error from 3 to 0.3? Given a sample size (n) with a margin of error of 3, calculate the additional number of samples required to reduce the margin of error to 0.3.

• How would you determine if the results of an AB test on a landing page redesign are statistically significant? Describe the process of analyzing AB test results to determine if the observed differences in click-through rates are statistically significant. Include steps for hypothesis testing and interpreting p-values.

• How would you build a model to predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, how would you develop a model to identify which merchants the company should target for acquisition when entering a new market?

• How would you assign point values to letters in Spanish Scrabble without knowing Spanish? If you need to build Scrabble for Spanish users and don't know Spanish, how would you determine the point values for each letter?

• What metrics/graphs/models would you use to analyze churn behavior for Netflix's pricing plans? Netflix has two pricing plans: $15/month or $100/year. An executive wants to analyze the churn behavior of users subscribing to either plan. What kinds of metrics, graphs, and models would you build to provide an overarching view of subscription performance?

• How would you build a model to predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, how would you build a model to predict which merchants the company should target for acquisition when entering a new market?

• How would you value the benefit of keeping a hit TV show on Netflix? Netflix executives are considering renewing a deal with another TV network for exclusive streaming rights to a hit TV series. The show has been on Netflix for a year. How would you approach valuing the benefit of keeping this show on Netflix?

• How would you measure and address the success of LinkedIn’s newsfeed ranking algorithm?

• How would you measure the success of LinkedIn’s newsfeed ranking algorithm?

• If some success metrics for the newsfeed algorithm are increasing while others are decreasing, how would you approach this situation?

• How would you determine if the results of an AB test for a landing page redesign are statistically significant? We want to launch a redesign of a landing page to improve the click-through rate using an AB test. How would you infer if the results of the click-through rate were statistically significant or not?

• Create a function calculate_rmse to compute the root mean squared error between predictions and target values. Write a function calculate_rmse to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

• Write a query to get the last transaction for each day from a table of bank transactions. Given a table of bank transactions with columns id, transaction_value, and created_at representing the date and time for each transaction, write a query to get the last transaction for each day. The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.

• Develop a function random_key to return a key based on weighted probabilities. Given a dictionary with weights, write a function random_key that returns a key at random with a probability proportional to the weights.

• Write a function to get a sample from a standard normal distribution. Create a function to generate a sample from a standard normal distribution.

• Create a function nearest_entries to find the closest element to N and surrounding elements in a sorted list. Given a sorted list of integers ints with no duplicates, write an efficient function nearest_entries that takes in integers N and k and finds the element of the list that is closest to N, returning that element along with the k-next and k-previous elements of the list.

Navigating the interview process at Freenome can be a mixed experience, as evidenced by the feedback provided. While initial phone interviews with technical and managerial staff were reported to be pleasant, subsequent follow-ups with HR were inconsistent and at times unprofessional. Despite these challenges, Freenome's mission and growth potential are undeniable.

As a high-growth biotech company with over $1.1B in funding and a strong focus on early cancer detection through innovative multiomics and machine learning platforms, Freenome presents a unique opportunity for scientists passionate about making a tangible impact on patient outcomes. The role of a Research Scientist at Freenome offers a chance to contribute to groundbreaking work in cancer screening, collaborating with interdisciplinary teams and leveraging cutting-edge technology.

If you're looking to join a mission-driven team determined to revolutionize cancer detection, Freenome is the place for you. To ensure you're fully prepared for the interview process, check out our main Freenome Interview Guide, where we cover a wide range of potential interview questions and company insights. At Interview Query, we equip you with the knowledge and confidence needed to excel in your interview journey.

Good luck with your interview!