Data Scientist Interview Questions

Data Scientist interview questions:

Data scientists go beyond the role of data analysts as they possess a deep understanding of how data analysis can drive critical decisions to enhance products and business outcomes. Proficient data scientists can adeptly assess the intricacy of various problem-solving approaches. They have the ability to suggest alternative solutions based on available resources and time constraints, enabling them to swiftly devise a simple yet functional solution when needed, and implement a more complex design within a larger time frame. These interview questions for data scientists encompass a candidate’s computer science background and their specific skill set relevant to the role.

There’s a data scientist role that places a significant emphasis on coding skills, targeting candidates with strong software engineering capabilities. These individuals understand the tools, processes, and requirements involved in creating and maintaining production-ready software. They possess robust programming skills in languages such as C++, Java, or Scala, have in-depth knowledge of databases, and have experience with deploying real-world machine learning solutions on platforms like Azure ML or PredictionIO. Moreover, this role often requires experience in working with big data and platforms like Apache Spark and Hadoop. While a computer science background is the ideal foundation for this role, candidates with engineering and mathematical backgrounds often develop practical software engineering skills to excel in this capacity.

An extensive data science interview typically comprises a blend of questions covering data science, big data, analytics, modeling, and analysis.

Computer Science questions:

Programming knowledge

• Do you actively participate in any open source projects within the programming community?
• What programming languages and development environments do you find most comfortable and proficient with?
• Have you utilized online machine learning platforms like Azure ML or PredictionIO in your data science work?
• Can you explain the process of training and deploying a logistic regression model? How about building a recommender system?
• Share details about a data science project you’ve been involved in that featured a substantial programming component.
• How would you approach the task of sorting a large list of numbers effectively?
• Define what hashing is, and provide an example of a situation where you might choose to use it.
• Explain the concepts of dynamic programming and recursion in the context of computer science and problem-solving.

Software engineering

• How do you approach the testing of your code, and what types of tests do you typically write to ensure code quality?
• What measures or techniques would you employ to continuously monitor the performance of a trained model and ensure that it does not deteriorate over time?
• If you needed to log computations made by your production model, how would you implement this functionality?
• Are you well-versed in version control, and which tools and processes have you used for version control in your work?
• Could you provide an overview of software design patterns, and which specific patterns are you familiar with? When would you consider using patterns such as Factory, Singleton, Memento, Builder, DAO, etc.?
• Have you been a part of a development team that adhered to a particular Agile methodology, and if so, which Agile processes have you worked with?
• What is technical debt, and how would you mitigate it, particularly in the context of deploying data-driven models in real-world scenarios?
• Describe the process of deploying a model trained in an environment like R. Are you acquainted with PMML (Predictive Model Markup Language)?

Role-specific questions:

Big data and distributed computing

• In the context of the map-reduce paradigm, can you explain the roles of the map function and the reduce function? What are the functions of the combiner and partitioner in this framework?
• How would you go about constructing a search engine designed to handle a vast collection of documents?
• Are you acquainted with technologies and components from the Hadoop ecosystem, such as Hadoop, Pig, and Hive?
• In which distributed environments have you gained experience and worked on projects?