Engineering Onsite Interview: Modeling

What are we looking out for?

Modeling Scientists at Quantcast build modelling and machine learning algorithms into production-ready modeling software and operate it, while our Software Engineers build the framework and infrastructure. Our Modeling Scientists demonstrate the ability to communicate, learn from our colleagues and work closely with Engineers to wrangle the data, build predictive models and provide insights.

A Modelling Scientist at Quantcast has a keen sense of machine learning and uses modelling as a substitute for direct measurement and experimentation to discover practical problems and implement cost-effective solutions.  

They leverage petabytes of data to answer business questions and improve business performance. This also involves exploring and examining data to find hidden patterns. With machine learning and artificial intelligence, our Modeling Science capability is changing the way in which we build large-scale systems, execute business strategies, and ultimately create value for our customers.

You will be expected to take on large-scale system design, from conceptualization to technology stack selection. Each decision of this process is expected to be backed up with data science.

The Quantcast DNA

  • Individuals in Quantcast are owners and experts who will & can make significant impact on the organisation
  • One constantly maintains the standard and raise the performance bar and ensure peers do the same.
  • Irrespective of title, everyone has the right & courage to voice their opinions & challenge decisions. Leaders alike are expected to respect these rights.
  • We are the face of Quantcast to our customers and always aim to deliver results on point.

Interview Tips

  • Topics that will be discussed in-depth:
    • Bayes Rule – You could be asked questions about Bayes’ theorem which you will be tested elementary on real-world problems than commercialized problems.
    • Probability – ­ You could be asked questions around probability theory using both Mathematics and computation.
    • Statistics – You will be recommended to brush up your knowledge on learning statistics with a heavy focus on coding up examples, preferably in Python or R.
    • Machine Learning – The interviewer will be looking for your real-life/practice experience in applying machine learning to datasets. We would love to hear about what real-life problems you have been tackling with machine learning at your previous job / in your spare time.
    • Problem Solving – We will expect you to run through your thoughts on how you solve certain problems and do involve the interviewers for these discussions. Be open-minded when it comes to discussion on whether there are right or wrong.  
  • Explain things in simple words, with simpler models, and simpler techniques.
  • You will be expected to solve probability and statistics problems, as well as logic puzzles.
  • These are technical interviews. Be prepared to solve problems at a substantially technical level.
  • Do a lot of practice problem solving with a pen and paper, just to make sure you are comfortable writing and speaking at the same time.You will be asked an extended question and we don’t expect you to have an immediate answer. We want you to explain your thoughts process. Be open to giving suggestions.
  • We would like to hear about your experience in applying machine learning to real-life problems, especially the trade-off /justification for your choice of model / algorithm.
  • Spend some time on LinkedIn understanding your interviewer’s background.

Pitfalls

  • Listen. And ask for clarifications.
  • Be open and honest. There is no need to pretend knowing anything if you, in fact, do not. We understand that everybody, including your interviewers, come from different background and have some blind spots.
  • Think out loud! And yes, you can ask for help and suggestions if you are stuck. Include the interviewers in the problem- solving process.

FAQs

Your interview is a conversation, we want you to leave our office knowing what the hiring manager wants, what the team is like, what your workday would be like and the tools, technologies you’d use to do your job. Have a strategy for asking your interviewer questions, here is what we recommend:   

  • Think of things you want to know about us before you will accept an offer, interview us for:
    • The position (responsibilities, challenges, career progression)
    • Team interactions (team structure, meetings, project management tools/ methodologies, interactions with other teams).
    • Development process (development time, code review, QA testing)
    • Open source, Codebase/ architecture (test suite, codebase documentation, build process/ automation, product hosting)
    • Tech stack: the rationale behind the stack? new tools, product voice (product ideas, meaty stuff pushed to release).  
    • Culture, Company, Competitors
  • Ask questions that are relevant to the person and the position you are interviewing for.

Additional Resources

Modeling techniques