Clinical Data Scientist

As a Clinical Data Scientist at Freenome, your main responsibilities will include cleaning and organizing clinical data from our studies, implementing informatics tools, and collaborating with various stakeholders to ensure the integrity of patient data. Additionally, you will analyze clinical data to monitor trends in subject enrollment and inform future study designs.

To qualify for the Clinical Data Scientist role at Freenome, candidates must hold a Bachelor’s or graduate degree in clinical informatics, bioinformatics, data science, or a relevant scientific field. Typically, you will need at least 5 years of related experience or 3 years with a Master’s degree, alongside proficiency in R or Python and familiarity with SQL.

Important skills for a Clinical Data Scientist at Freenome include strong analytical and problem-solving abilities, experience in clinical research data management, and proficiency in data analysis technologies such as R or Python. Communication and collaboration skills are also essential in navigating the interdepartmental cooperation Freenome values.

Freenome supports its Clinical Data Scientists by fostering a collaborative environment where your expertise is valued. You will work with various teams to develop cutting-edge solutions and have the opportunity to conduct impactful analysis that directly contributes to advancing our cancer screening tests.

At Freenome, Clinical Data Scientists can expect substantial career growth opportunities. As Freenome continues to expand, you will have the chance to take on larger projects, lead initiatives, and contribute significantly to our mission to revolutionize cancer detection.

When answering this question, share specific projects where you've analyzed clinical data. Highlight your role, the tools you used, and any significant outcomes that came from your analysis to demonstrate your impact on the projects.

Explain your approach to data validation and reconciliations, referencing any specific processes or tools you've utilized previously to track inconsistencies and ensure a high-quality dataset.

Mention your proficiency in R or Python, providing examples of how you've applied these programming languages in past projects, especially in clinical research contexts. Discuss any libraries or frameworks you used to enhance your data analysis.

Share your strategies for prioritizing tasks and managing project timelines effectively. Illustrate with examples of how you've met tight deadlines in previous roles while maintaining data integrity.

Describe a specific dataset that was particularly challenging, what made it difficult, and the steps you took to clean, organize, and analyze it, showcasing your problem-solving skills.

Talk about your communication style and how you ensure everyone is aligned on data-related projects. Provide examples of how collaboration has led to successful outcomes in your past roles.

Discuss tools such as Tableau or custom Python/R graphics libraries you’ve used to visualize complex datasets. Share how these visualizations helped stakeholders understand the findings.

Highlight specific clinical informatics tools you've used and the roles they played in your previous projects. Show how these tools have improved the management and analysis of clinical data.

Your response should reflect your personal passion and connection to the field of cancer research, discussing how your work contributes to improving patient outcomes and the impact it has on your motivation.

Mention resources like journals, conferences, and online courses that you engage with to stay current in the fields of clinical data science and cancer research, showcasing your commitment to continuous learning.