An Applied Scientist at Cohesity plays a pivotal role in harnessing data science and machine learning to drive innovative solutions that enhance data management and protection. This position is crucial as it directly impacts the development of advanced features within Cohesity’s products, shaping the way customers interact with their data environments. You will be expected to tackle complex problems, transforming data into actionable insights that improve customer experiences and operational efficiencies.

The role involves collaborating closely with cross-functional teams, including engineering, product management, and operations, to design and implement algorithms that solve real-world challenges in data management. You will contribute to projects that involve large-scale data processing, predictive analytics, and machine learning model development, making your work both impactful and strategically significant in the broader context of Cohesity’s mission to simplify data management and protect against data loss.

Common Interview Questions

Candidates can expect a variety of questions during the interview process, reflecting the diverse skill set required for the Applied Scientist role. The following questions are representative examples drawn from 1point3acres.com and are grouped into relevant categories to illustrate common patterns:

Technical / Domain Questions

This category assesses your technical expertise and understanding of applied science concepts.

• Explain the differences between supervised and unsupervised learning.
• What are some common techniques for handling missing data?
• Describe a time you implemented a machine learning model. What challenges did you face?
• How do you evaluate the performance of a machine learning model?
• Discuss a recent advancement in data science that excites you.

Problem-Solving / Case Studies

Expect to engage in case studies or problem-solving scenarios that reflect real-world challenges.

• Given a dataset, how would you approach building a predictive model?
• How would you balance model accuracy with computational efficiency?
• Describe a complex problem you solved using data analysis. What was your approach?

Behavioral / Leadership Questions

These questions gauge your soft skills and cultural fit within the team.

• Describe a situation where you had to influence a decision. How did you handle it?
• Give an example of a time you worked on a team project. What was your role?
• How do you prioritize tasks when faced with multiple deadlines?

Coding / Algorithms

Prepare for coding challenges that test your algorithmic thinking and coding skills.

• Write a function to implement a binary search algorithm.
• How would you optimize a sorting algorithm for large datasets?
• Discuss the time complexity of various search algorithms.

Key Responsibilities

As an Applied Scientist at Cohesity, your day-to-day responsibilities will revolve around developing and implementing data-driven solutions that enhance product offerings. You will be expected to:

• Collaborate with engineering and product teams to design algorithms that improve data management features.
• Analyze large datasets to extract insights that inform product decisions and enhance customer experiences.
• Develop machine learning models and validate their effectiveness through rigorous testing and evaluation.
• Document your findings and present results to stakeholders, ensuring clarity and actionable insights.

Your work will directly contribute to the success of Cohesity's products, impacting how customers manage and protect their data.

Role Requirements & Qualifications

To be a competitive candidate for the Applied Scientist position at Cohesity, you should possess the following qualifications:

• Must-have skills:

  • Strong understanding of machine learning and statistical methods.
  • Proficiency in programming languages such as Python, R, or Java.
  • Experience with data visualization tools and libraries.

• Nice-to-have skills:

  • Familiarity with big data technologies (e.g., Hadoop, Spark).
  • Knowledge of cloud computing platforms (e.g., AWS, Azure).
  • Prior experience in agile development environments.

Frequently Asked Questions

Q: What is the interview difficulty level and how much preparation time is typical? The interview process is considered challenging, particularly for technical topics. Candidates typically spend several weeks preparing to ensure they are well-versed in both technical and behavioral aspects.

Q: What differentiates successful candidates? Successful candidates demonstrate a strong technical foundation, effective problem-solving skills, and the ability to communicate complex ideas clearly. They also show alignment with Cohesity's values and culture.

Q: What is the typical timeline from initial screen to offer? The timeline can vary, but candidates generally expect to complete the process within a few weeks, depending on scheduling and team availability.

Q: What are the culture and working style at Cohesity? Cohesity fosters a collaborative and innovative work environment, encouraging employees to share ideas and work together to solve problems. Adaptability and a proactive approach are highly valued.

Q: Are there remote work or hybrid expectations? Cohesity has embraced flexible work arrangements, allowing employees to work remotely or in a hybrid model, depending on team needs and personal circumstances.

Other General Tips

• Understand Cohesity’s Products: Familiarize yourself with Cohesity’s product offerings and how they address data management challenges. This knowledge demonstrates your genuine interest in the company.
• Practice Coding Problems: Utilize platforms like LeetCode or HackerRank to sharpen your algorithm and coding skills, as technical assessments are a key part of the interview.
• Prepare STAR Responses: Use the STAR method (Situation, Task, Action, Result) to structure your answers for behavioral questions, making your responses clear and impactful.
• Engage with Interviewers: During interviews, ask clarifying questions and engage in discussions to showcase your curiosity and collaborative mindset.