Senior ML Engineer/Data Scientist (ZDX)

As a Senior ML Engineer/Data Scientist at Zscaler, you will lead initiatives focused on enhancing user digital experiences. Your key responsibilities will include identifying performance issues through model building, designing predictive models to forecast user behavior, implementing advanced AI/ML algorithms for anomaly detection, managing the complete machine learning project lifecycle, and creating compelling data visualizations to communicate insights to diverse audiences.

To qualify for the Senior ML Engineer/Data Scientist role at Zscaler, candidates should possess a Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field, along with a minimum of 3 years of professional experience in data science. Proficiency in Python, TensorFlow, SQL, and familiarity with Generative AI and NLP techniques are essential for success in this role.

Successful candidates for the Senior ML Engineer/Data Scientist position at Zscaler should demonstrate expertise in multi-dimensional anomaly detection algorithms, particularly with time series data. Experience in building and deploying ML models in production is also vital, along with familiarity in using orchestration tools like Kubernetes and Airflow.

Zscaler is recognized as a Best Workplace in Technology and fosters an inclusive and supportive culture. As a Senior ML Engineer/Data Scientist, you’ll have the opportunity to work alongside some of the brightest minds in the industry, engage in innovative projects, and contribute to making cloud security more accessible and user-focused. Plus, Zscaler offers comprehensive benefits and values diversity within its workforce, making it a fantastic employer.

Zscaler emphasizes the importance of continuous learning and professional development. Senior ML Engineers/Data Scientists are encouraged to engage in educational opportunities, collaborate on innovative projects, and explore new technologies. With access to mentorship and a dynamic work environment, Zscaler supports continuous growth and expertise in the fast-evolving field of data science.

In discussing your experience, highlight specific projects where you designed, trained, and deployed machine learning models. Use metrics to showcase the impact of your models and how they improved performance or user experience. Discuss tools you’ve used like Python or TensorFlow, and emphasize your role in each project.

Outline your methodology for pinpointing performance issues, such as data analysis techniques, models you've built, or metrics you track. Describe how you collaborate with cross-functional teams to resolve these issues, ensuring that you emphasize outcomes and improvements made as a result.

Discuss your preferred algorithms for detecting anomalies, such as statistical methods or machine learning techniques. Explain why you choose these algorithms based on their effectiveness in relevant scenarios, particularly focusing on time series data and how you've applied them in past roles.

Share a specific example of a data visualization project, focusing on the objective and audience for the visualization. Discuss the tools you used, such as Tableau or Matplotlib, and how the visualization contributed to actionable insights. Explain the feedback you received from stakeholders.

Describe your strategies for keeping up-to-date with industry trends, such as following relevant blogs, attending webinars, or participating in online communities. Mention any courses you've taken or events you've attended to demonstrate your commitment to ongoing education.

Discuss your experience with the deployment process for machine learning models. Highlight the tools you've used, such as Kubernetes or MLflow, and the challenges you've faced. Emphasize how you ensured that the models maintained performance and reliability after deployment.

Share a specific example where you worked with a technical and non-technical team. Discuss how you translated complex technical concepts into understandable terms and how you ensured effective communication. Highlight the project's success and any insights you gained from the collaboration.

Express your perspective on the impact of data science in user experience improvement. Discuss how data-driven insights can help tailor experiences, meet user needs, and increase satisfaction, giving examples from your past projects to illustrate the value data science brings.

Explain your data cleaning process and techniques you employ to handle dirty or inconsistent data. Mention specific tools you’ve used for data preprocessing and how maintaining clean data contributes to the accuracy of your models.

Identify specific challenges you've faced in past data science projects, such as data scarcity or model performance issues. Discuss how you approached these challenges, the solutions you implemented, and what you learned from resolving them to demonstrate your problem-solving skills.