Senior Applied Scientist / Data Scientist

As a Senior Applied Scientist / Data Scientist at Zscaler, your key responsibilities will include designing and implementing machine learning models, particularly focusing on large language models. You'll also conduct in-depth research in generative AI and predictive analytics, collaborate with cross-functional teams to integrate AI technologies, and analyze large datasets to provide meaningful insights that enhance performance and drive business decisions.

To qualify for the Senior Applied Scientist / Data Scientist role at Zscaler, candidates should hold an advanced degree in computer science, machine learning, or statistics, ideally at the PhD level. A minimum of 5 years of experience in NLP is crucial, alongside proficiency in programming languages like Python and SQL, and familiarity with ML frameworks such as TensorFlow and PyTorch.

Zscaler effectively utilizes machine learning to enhance its cloud security offerings. Through advanced models, including LLMs, the company aims to improve the intelligence of its external and internal tools, thereby increasing efficiency. By leveraging techniques in anomaly detection and predictive analytics, Zscaler can better analyze data for actionable insights, positioning it at the forefront of cybersecurity innovation.

The Senior Applied Scientist / Data Scientist at Zscaler will work with various advanced technologies including large language models (LLMs), generative AI techniques, and big data platforms such as Hadoop and Spark. Familiarity with cloud services like AWS, Azure, and GCP will also be beneficial in this role, as these technologies form the backbone of our innovative cloud solutions.

Zscaler is recognized for its commitment to innovation, demonstrated through its pioneering Zscaler Zero Trust Exchange™ platform. With a strong emphasis on machine learning and AI, the company continually explores new methodologies to enhance business models and cybersecurity. Joining Zscaler means being part of a team that values cutting-edge technology and fosters a culture of collaboration and growth.

When answering this question, focus on a specific project where you implemented a complex model, detailing the challenges faced, the methodologies applied, and the results achieved. Highlight your role in the project, emphasizing collaboration and the impact of the model on the business.

Discuss your direct experience with natural language processing, citing specific applications or projects. Talk about generative AI techniques you have utilized and how they contributed to the success of that project, showcasing your understanding of the technologies involved.

Explain your process for validating models, including methods for cross-validation and the importance of performance metrics. Share examples of how you have ensured model robustness and reliability in previous roles to provide insight into your analytical approach.

Offer a clear narrative of a time when your data analysis led to a significant business outcome. Describe the context, the insights you provided, and how they were used to make informed decisions, demonstrating your ability to connect data with strategic thinking.

Discuss your proficiency with ML frameworks such as TensorFlow and PyTorch, highlighting specific projects where you utilized these tools. Explain how these frameworks facilitated your work, including any challenges you overcame using them in a practical context.

Provide examples of statistical methods you’ve employed in past projects, such as regression analysis or clustering techniques. Discuss how these methods supported your analyses and contributed to model development, showcasing your analytical rigor.

Talk about your methods for staying informed, such as following industry leaders, attending conferences, or engaging with academic journals. Highlighting your commitment to continuous learning indicates your passion for the field and readiness to adopt emerging technologies.

Discuss your experience working within teams, emphasizing collaborative approaches to problem-solving. Provide examples of successful teamwork scenarios and how communication and joint effort led to project success, reflecting your ability to thrive in Zscaler’s collaborative environment.

Discuss your understanding of data privacy regulations and ethical considerations in data science. Provide examples of how you have ensured data is handled responsibly and within legal frameworks in your past roles, showcasing your integrity and professionalism.

Reflect on your understanding of the challenges in applying AI and ML to cybersecurity, such as data quality, scalability, and bias in algorithms. Provide thoughtful insights on how these challenges could be mitigated, demonstrating your critical thinking and industry awareness.