Tiger Analytics is a leading advanced analytics consulting firm, trusted by multiple Fortune 500 companies for its expertise in Machine Learning, Data Science, and AI. Renowned by industry analysts such as Forrester and Gartner, Tiger Analytics helps organizations generate business value from their data.

As a Machine Learning Engineer, you'll join a dynamic team of experts driving business results through scalable, high-performance ML systems. Your role will involve developing and deploying data science solutions, building reusable production data pipelines, and collaborating with cross-functional teams to ensure business value.

This guide will walk you through Tiger Analytics' interview process and help you prepare effectively. Let's get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Tiger Analytics as a Machine Learning Engineer. Whether you were contacted by a Tiger Analytics 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 Tiger Analytics 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 Tiger Analytics 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 Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Tiger Analytics Machine Learning Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around coding tasks, data analysis, and machine learning concepts.

Python coding questions were prominent in previous interviews, often focusing on areas like data analysis. Be prepared to solve problems and write code on the spot.

Depending on the specific requirements of the role, additional technical questions might include areas such as Kubernetes and cloud environments.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day, likely including technical deep-dives and behavioral assessments.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Machine Learning Engineer role at Tiger Analytics.

Quick Tips For Tiger Analytics Machine Learning Engineer Interviews

• Emphasize Python Proficiency: Python coding questions are a significant part of the interview process. Practicing coding problems and familiarizing yourself with data analysis in Python will be beneficial.
• Understand Real-World ML Applications: Being able to create scalable ML systems and understanding how to deploy and monitor them are crucial. Make sure you are well-versed in these areas.
• Brush Up on Cloud and Big Data Technologies: Knowledge of tools like Kubernetes, Hadoop, and cloud platforms (like AWS) is often tested. Ensure you are comfortable with these technologies.

Typically, interviews at Tiger Analytics vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• Write a function missing_number to find the missing number in an array of integers from 0 to n. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array.

• Write a function to return the median value of a list of sorted integers where more than 50% of the list is the same integer. You're given a list of sorted integers in which more than 50% of the list is comprised of the same repeating integer. Write a function to return the median value of the list in (O(1)) computational time and space.

• Write a function min_distance to find pairs of elements with the minimum absolute distance in an array. Given an array of integers, write a function min_distance to calculate the minimum absolute distance between two elements then return all pairs having that absolute difference. Ensure the pairs are returned in ascending order.

• Create a function digit_accumulator to sum all digits in a string representing a floating-point number. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

• Write a function n_frequent_words to find the top N frequent words in a paragraph. Given an example paragraph string and an integer N, write a function n_frequent_words that returns the top N frequent words in the posting and the frequencies for each word. Also, determine the function run-time.

• How would you find the median of a list where more than 50% of the elements are the same? You are given a list of sorted integers where more than 50% of the list is comprised of the same repeating integer. Write a function to return the median value of the list in (O(1)) computational time and space.

• How would you generate a list of prime numbers up to a given integer N? Given an integer N, write a function that returns a list of all prime numbers up to N. Return an empty list if there are no prime numbers less than or equal to N.

• - How would you assess the validity of an AB test result with a 0.04 p-value? Your company is running a standard control and variant AB test to increase conversion rates on the landing page. The PM finds a p-value of 0.04. How would you determine if this result is valid?

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice for this problem?

• How would you evaluate the performance of a decision tree model before and after deployment? If you decide to use a decision tree model, how would you assess its performance both before deployment and after it is in use?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss why one might choose random forest over logistic regression for certain problems.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which scenarios would you prefer a bagging algorithm over a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? If your manager asks you to build a neural network model to solve a business problem, how would you justify the complexity of the model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? Assume you have built a V1 of a spam classifier for emails. What metrics would you use to monitor the accuracy and validity of the model?

What is the average salary for a Machine Learning Engineer at Tiger Analytics?

Q: What is the interview process at Tiger Analytics like?

A: The interview process at Tiger Analytics typically involves three rounds. You'll encounter a mix of technical and coding challenges focused on Python and data analysis. The process may also include some role-specific questions that evaluate your fit with company needs. Prepare by practicing Python coding and data analysis problems.

Q: What skills are essential for a Machine Learning Engineer position at Tiger Analytics?

A: Essential skills include proficiency in Python, Spark, Hadoop, Docker, and other machine learning frameworks like Scikit-learn, TensorFlow, and Keras. You'll also need experience with cloud platforms such as AWS SageMaker, and the ability to manage data pipelines, troubleshoot production issues, and collaborate effectively with cross-functional teams.

Q: What kind of projects would I work on at Tiger Analytics?

A: At Tiger Analytics, you'll work on deploying, executing, validating, monitoring, and improving data science solutions. You’ll also create scalable machine learning systems, build production data pipelines, and write production-quality code and libraries that can be packaged as containers and deployed.

Q: What is the company culture like at Tiger Analytics?

A: Tiger Analytics boasts a fast-growing, advanced analytics consulting environment that values innovation, deep expertise, and effective communication. You’ll collaborate with cross-functional teams to bring business value from data, all while enjoying significant career development opportunities in a challenging and entrepreneurial setting.

Q: How should I prepare for an interview at Tiger Analytics?

A: To prepare for an interview at Tiger Analytics, research the company and prepare by practicing Python coding and data analysis questions. Websites like Interview Query can be especially helpful to review the types of questions that might be asked and to practice efficiently.

Conclusion

Interviewing for the Machine Learning Engineer position at Tiger Analytics is a rigorous but rewarding process. With a challenging interview structure comprising three detailed rounds, candidates should come prepared, especially in Python coding and data analysis. Despite the difficulty, candidates have reported a generally positive experience.

Tiger Analytics offers a vibrant environment for Machine Learning Engineers to grow, providing solutions for scalable, high-performance systems and collaborating with cross-functional teams. The role demands expertise in Python, Spark, Hadoop, Docker, and a deep understanding of ML frameworks like Scikit-learn and TensorFlow.

If you want more insights about the company, check out our main Tiger Analytics Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Tiger Analytics’ interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Tiger Analytics machine learning engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!