Our Top Five Tips For Your Data Scientist Interview

Wesley Taupin our consultant managing the role
Posting date: 3/28/2019 11:38 AM
The role of Data Scientist is one of the most in-demand jobs in the tech world now. But, given that it is still a relatively new job, in a relatively new field, a lot of companies are still struggling to source enough quality candidates for their team.

Despite the demand, tech companies are very specific about the candidates they’d like to hire. Passing a Data Science interview can be very tricky, especially considering that businesses are looking for the right technical knowledge, business sense, and culture fit. 

With this in mind, here are five key tips for the Data Science interviewing process. By making sure you are prepared for the below, you’ll be able to ensure that you don’t sell yourself short.

Have A Concise Overview Of Your Project Experience


It’s imperative you prepare an overview of your successful Data Science projects. Hiring Managers aren’t interested in getting into every detail of your completed projects, but they do want to know that you have the right experience. Focus on key factors, highlighting the types of projects you’ve been working on and the successes you had as a result of those projects. Keep your achievements clear and concise.

Show Your Communication Skills


A good Data Scientist is more than just a good programmer. You need to be able to show that you can translate your findings into insights that can be understood by non-technical people in the business. During your interview, Hiring Managers may test your ability to step away from role-specific language. This is to asses whether you know how to engage with non-technical colleagues and parts of the business who may not understand the value of Data Science to the company.

Bring Out Your USPs


Companies will potentially be interviewing several candidates for a specific role, so it’s important that you are able to stand out. Consider what you have achieved that your fellow interviewees may not have. One good way to stand out is to have articles published on popular Data Science websites/blogs. From my experience, Hiring Managers see this as a big plus and it makes for a great talking point during the interview process. If you are looking to do this, you should always choose a unique topic and not something that is already explored a lot by others. On a similar note, you could highlight the Data Science projects you’ve achieved outside of work through platforms such as Kaggle.

Know Your Computer Science Fundamentals


Having a decent knowledge of Computer Science fundamentals, like algorithms is essential, especially if you are interviewing with tech companies. Whilst there are other elements to the role, you can expect questions related to programming, so for a Junior Data Scientist, I’d recommend practicing coding for a few days before your interview (if you are not doing this already in your day to day job).

Have An Understanding Of How You’ll Fit In At The Company


For some Tech companies, particularly start-ups, cultural fit is just as important, if not more so, than how good you are at coding. They’ll want to understand how you would react to different scenarios at work and whether or not you share the values of their company. For this reason, don’t be surprised to see a few team members join the interview as they look to see how you’ll fit in. Make sure you take a look at the company’s website, read their blogs and articles, and check their social media feeds in advance so that you have a good understanding of what the business is like. Remember, culture is a two-way fit so it’s about making sure the business is right for you, as much as it is right for them.

The interviewing process can be tricky but, at Harnham, our expert consultants are here to support you through the entire recruitment process. We will always make sure you are prepared for your interview and will run you though the topics you can expect to come up.

If you’re looking to take the next step in your Data & Analytics career, take a look at our latest opportunities or get in touch with one of our expert consultants to learn more. 

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Weekly News Digest - 11th-15th Jan 2021

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of data and analytics. KDNuggets: 20 core Data Science concepts for beginners The field of Data Science is one that continuously evolves. For Data Scientists, this means constantly learning and perfecting new skills, keeping up to date with crucial trends and filling knowledge gaps.  However, there are a core set of concepts that all Data Scientists will need to understand throughout their career, especially at the start. From Data Wrangling to Data Imputation, Reinforcement Learning to Evaluation Metrics, KDNuggets outlines 20 of the key basics needed.  A great article if you’re just starting out and want to grasp the essentials or, if you’re a bit further up the ladder and would appreciate a quick refresh.  Read more here.  FinExtra: 15 DevOps trends to watch in 2021 As a direct response to the COVID-19 pandemic, there is no doubt that DevOps has come on leaps and bounds in the past year alone. FinExtra hears from a wide range of specialists within the sector, all of whom give their opinion on what 2021 holds for DevOps.  A few examples include: Nirav Chotai, Senior DevOps Engineer at Rakuten: “DataOps will definitely boom in 2021, and COVID might play a role in it. Due to COVID and WFH situation, consumption of digital content is skyrocket high which demands a new level of automation for self-scaling and self-healing systems to meet the growth and demand.” DevOps Architect at JFrog: “The "Sec'' part of DevSecOps will become more and more an integral part of the Software Development Lifecycle. A real security "shift left" approach will be the new norm.” CTO at International Technology Ventures: “Chaos Engineering will become an increasingly more important (and common) consideration in the DevOps planning discussions in more organizations.” Read the full article here.  Towards Data Science: 3 Simple Questions to Hone Python Skills for Beginners in 2021 Python is one of the most frequently used data languages within Data Science but for a new starter in the industry, it can be incredibly daunting. Leihua Yea, a PHD researcher at the University of California in Machine Learning and Data Science knows all too well how stressful can be to learn. He says: “Once, I struggled to figure out an easy level question on Leetcode and made no progress for hours!” In this piece for Towards Data Science, Yea gives junior Data Scientists three top pieces of advice to help master the basics of Python and level-up their skills. Find out what that advice is here.  ITWire: Enhancing customer experiences through better data management From the start of last year, businesses around the globe were pushed into a remote and digital way of working. This shift undoubtedly accelerated the use of the use of digital and data to keep their services as efficient and effective as possible.  Derak Cowan of Cohesity, the Information Technology company, talks with ITWire about the importance of the continued use of digital transformation and data post-pandemic, even after restrictions are relaxed and we move away from this overtly virtual world.  He says: “Business transformation is more than just a short-term tactic of buying software. If you want your business to thrive in the post-COVID age, it will need to place digital transformation at the heart of its business strategy and identify and overcome the roadblocks.” Read more about long-term digital transformation for your business here.  We've loved seeing all the news from Data and Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

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