DSF Discussion /
How to Ace Your Data Science Interview
30 Nov 2017 01:30:35 PM
With dissertation deadlines glooming, Data Science students are gearing up to leave the academic world and find their feet in a data science role. We all know the demand for the skills and the shortage supply of experienced data scientists means there are opportunities everywhere and companies are looking to secure grad talent, so finding some data science jobs should not be too difficult.
But before you reach that commercial goldmine, your faced with the job interview. Not matter how much experience and exposure you have in previous interviews, public speaking or Data science discussions, this preparation is still hard.
Data Science interviews tend to cover a wide range of topics. From technical exposure, to statistical understanding, to solving and communicating complex business problems.
At Eden Smith we work with a number of business in hiring across the Data science spectrum and to support you ace your interview have curated a list of common Data Science interview questions. We have enriched this data with information from online and insight from our Data Science partners to help you prepare for the types of questions that can be thrown at you during your Data Science Interview.
Building data models for machine learning or pure data transformation and analysis is one of the most common tasks of the modern data scientist and more and more businesses are developing teams, particularly with grads, that are modelling and coding heavy, this is resulting in more interviews covering the various modelling techniques and statistical theories.
Not all interviews will be technical, but below are some questions that will help you prepare and refamiliarize yourself with.
Most Data Science teams are involved in both the ingestion of data for modelling and analysis and the production of models into the enterprise environment. Whether this is led by Data Engineering, software engineering or a database development team, you will be expected to have a strong understanding of various program languages, those directly involved in Data Science and those surrounding data integration and exportation. Be sure to brush up on your Python, R, SQL and relevant big data programming languages such as Scala.
Data Science Process
Although being hands on with data and modelling and programming are the major aspects of any data science in today’s world; often businesses are looking to understand how insights and results are created. Interviewers are looking for you to demonstrate a clear understanding and be able to explain various methods and processes throughout a data science project and their Pros. Cons and use cases to a non-technical audience. Practice articulating and giving clear simple explanations of various complex data science procedures.
Data Science is still a position that has great variety and a lack of standardisation across the market. Therefore, every Data Science position and company you interview for will take a slightly different approach and expect additional skills and awareness of the surrounding subjects. Be sure to explore the business your interviewing with, check current employees; data scientists and analysts and see what additional products, technologies and soft skills they have experience with. Some common general questions are:
If you want more advice or support in how to land your dream data science opportunity or if you’re a manager looking to scale a data science team get in touch with us today.
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