Did you know that data science is one of the fastest-growing business segments in India?
- Data science jobs have grown more than 650% since 2012
- Jobs in data science will escalate by 27.9% by 2026
- The market is predicted to grow to USD 322.9 billion by 2026
- India is expected to capture 32% of the big data market worldwide
- India has contributed 9.4% of total global analytics job openings (up from 7.2%)
- Analysts predict 11 million job openings in data science by 2026 in India alone
- Nearly 74.5% of analytics and data science comes from large Indian firms
Before we help you interview data scientist job candidates, here’s a quick understanding of what data science is and how it works.
What is data science?
Data science is the process of simplifying and separating large amounts of data in order to find patterns that drive business decisions. To analyze all this information, data scientists apply skills in mathematics, statistics, artificial intelligence (AI), and/or computer engineering. Once they’ve pulled all the information they need, they can determine which events happened, why they happened, and when they might happen again.
In healthcare, a data scientist might:
- Analyze data from clinical trials to see how people react to a prescription drug
- Input data to chatbots or AI platforms so patients can diagnose themselves
- Track the movements of professionals to streamline actions and improve practices
As you can see, healthcare organizations could use data science to make their businesses more efficient or solutions more scientifically substantiated.
How to interview data scientist job candidates
Now that you have more context, let’s look at some interview questions to ask—and answers to anticipate—when interviewing data scientist job candidates.
1- What are some differences between big data and data science?
Big data represents all the data or statistics that big businesses need to recognize patterns and identify trends. Big data is all about handling the data.
Data science is the action of digging, capturing, analyzing, and applying the data to predict outcomes or prescribe solutions. Data science is all about the science of the data.
Consider asking your interviewees which tools they would use in big data versus data science. Big data tools include Hadoop, Spark, and Flink. Data science tools include SAS, R, and Python.
Also keep an ear out for the phrases “building the model,” “validating the model,” or “deploying the best model” in data science.
- To build the model is to capture the data.
- To validate the model is to analyze the data.
- To deploy the model is to use the data.
2- Which deep learning modules are you most familiar with?
According to MathWorks, deep learning is a machine learning technique that teaches computers how to learn by example. For instance, deep learning technology empowers driverless cars by enabling them to recognize stop signs or differentiate a pedestrian from a lamppost. It’s also used to activate voice controls on our phones, tablets, and TVs.
During your interview, determine whether your job candidate is familiar with any of these common deep learning modules:
- TensorFlow: Open-source platform developed by Google
- PyTorch: Open-source platform developed by Facebook
- Keras: Open-source framework compatible with Microsoft Cognitive Toolkit (among others)
- Sonnet: High-level library used for building structures in TensorFlow
- MXNet: Open-source framework that supports multiple programming languages
- Swift for TensorFlow: Next-gen platform that enhances TensorFlow with its own programming language
- Gluon: Open-source interface that makes it easier to build machine learning models
- Deeplearning4J (DL4J): Distributed deep learning library written for Java and JVM (Java Virtual Machine)
- Open Neural Network Exchange (ONNX): An open ecosystem designed by Microsoft and Facebook
- Chainer: Open-source framework written in Python on top of NumPy and CuPy libraries
3- In your own words, tell me what “fsck” means.
The letters “fsck” are an abbreviation for “file system check.” A data scientist would “run” a fsck to check and interactively repair inconsistent file systems.
If a file system is consistent, the fsck command will report on the number of files, used blocks, and free blocks in the file system.
If a file system is inconsistent, the fsck will display information about the inconsistencies found and prompt the data scientist for permission to repair them.
Other fsck-related questions could be, “Tell me about a time when you ran a successful file system check,” or “What is the most unusual or difficult file system check you have ever experienced?”
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