In the modern business environment, nearly everything can be turned into data and analyzed by experts. Data science can have a huge impact on every area of your business, from marketing and sales to HR and software development. There is no industry that can hide from being measured and influenced by data. Professionally built machine learning can even help medical patients understand better their health condition and bank clients review their spendings. More generally, however, businesses use data science for things like:
- Improving their decision-making by turning it into a data-based rather than instinct-driven process.
- Understanding the ever-changing market and adapting products to the current requirements.
- Optimizing costs by predicting what is truly worth investing in (testing ideas).
- Finding the perfect audience for products.
- Improving cybersecurity, detecting anomalies.
- Creating an offering more tailored to specific customers.
What’s more, thanks to the technological advancements, we no longer think of data as something that only describes the past or present. In fact, the power lies in how data can help with predicting the future.
As data became the new trend, the business started changing their attitude from „why hire data scientists?” to „how can I hire data scientists?”. We’ve already answered the first question — time to answer the second one, a more important one, as data scientists are now in constant demand, and finding one that truly fits your business might be tricky.
What does a data scientist actually do?
Data scientists work on multiple fields related to data in order to deliver the best possible predictions for the business they work for. Their job responsibilities might differ depending on multiple factors, such as what other people are included in the data team (we delve into this in the next section of our guide) and the current needs of the company.
However, data scientists most often…
- Build machine learning algorithms that help predict the future.
- Analyze the performance and accuracy of predictive models introduced.
- Use advanced data visualization techniques to present findings to stakeholders, project managers, and other relevant executives.
- Based on data, propose solutions to issues and new paths for the business.
- Determine the root cause of the current issues.
- Mine data and choose the elements to be analyzed by the models.
Why do companies hire data scientists, data analysts, and data engineers?
For the best understanding of the role of data scientists, it’s also important to clearly divide them from data analysts and data engineers. These three terms are sometimes used interchangeably which shouldn’t be the case.
Unlike data scientists, data analysts most often do not build machine learning or any other sophisticated data-related models. They are usually analyzing data provided to them by others, often from one source, in order to get new insights on current problems. On the other hand, data scientists leverage machine learning and other relevant technologies to try to predict the future. To do it, they incorporate in their work data from multiple sources, often disconnected.
How about data engineers? They are the people who mine the data from different sources and build pipelines to deliver this data to people who will later incorporate it in their research, such as data analysts or data scientists. Data scientists might also mine data themselves if separate data engineers are not present at the company.
Most important hard skills for data scientists
When you want to hire data scientists, you should first and foremost think about whether they have the skills to deliver the results you need. Beware of data analysts who apply for the job — many of them might not have the hard skills needed for a data scientist role. What are those skills?
- Knowledge of machine learning and other relevant concepts (such as AI, deep learning, GAN).
This should not be only theoretical knowledge but also actual experience in building predictive ML models. A data scientist you want to hire should also understand the differences between different approaches to machine learning (e.g. supervised and unsupervised).
- Understanding of complex mathematical concepts, such as probability or algorithms.
- Proficiency in at least one programming language used for machine learning.
Choices vary but most commonly include Python, C++, Java, R, Scala. If you already have a data team, it’s best to hire a data scientist using the same technology as they do.
- Experience with relevant frameworks and libraries.
The choice of these depends on the programming language used and the specific needs of a data scientist. For example, Caffe is one of the most popular choices for deep learning, while Orange3 is useful for data mining and data visualization.
- Experience with data mining, data analytics, and other relevant operations on data, including database management using SQL, NoSQL, etc.
Crucial soft skills for data scientists
Although the job of a data scientist is surely a very technical one, a set of specific soft skills also comes in handy. They support not only the efficiency and quality of one’s work but also have a great impact on team collaboration and innovation.
What soft skills should you be specifically looking for when you plan to hire data scientists?
- Attention to details
Even a small mistake when preparing a machine learning model can dramatically change the results delivered. As analyzing the performance and accuracy of ML models is one of the most important jobs of a data scientist, attention to detail is a must-have skill.
- Ability to communicate complex ideas in a simple way
It’s incredibly important for a data scientist to be able to communicate well with others in the data team. However, as they are also often presenting their predictions to non-technical teams, being able to skip the jargon and present complex ideas to less tech-savvy people is also important.
- Researcher attitude
Where to find data to analyze? Which data to choose? What are the ways to improve the performance of the ML models? To truly thrive, data scientists have to always be curious about their project and how to improve it based on the latest advancements in the industry
The educational background of top data scientists
Best data scientists are usually graduates (often with a Master’s degree) in fields like computer science, statistics, maths, physics, or various types of engineering. They usually make use of the time in academia by taking courses on topics like:
- Various aspects of mathematics and applied mathematics.
- Cognitive science.
- Econometrics.
- AI, machine learning, and other relevant subjects.
- Programming.
- Data analysis, data mining, and other operations on data.
- Robotics.
- Business analysis.
When you want to hire data scientists, it’s also important to assess their experience. Focus on the candidates who have already worked in a business environment (instead of only in academia) and even more specifically on those who cooperated with companies within your industry.
Top data scientist job interview questions
To assess the skills and knowledge of your data scientist candidate, running a technical interview is a must. Such an interview can take different forms, e.g. a set of questions to be answered or a task that can be completed by the candidate at home. Whatever is your choice, if you are not a data expert yourself, you should ask a specialist to help you with running the interview. This can be either another data scientist or data-focused software consultant/IT Project Manager.
What kind of questions can be asked during a data scientist job interview?
- What are the differences between SQL and NoSQL? When one is preferred over the other?
- How would you approach transferring a 1.000.000+ gigabytes log file into a database?
- What is naïve Bayes? Why do we call it “naïve”?
- What is the Curse of Dimensionality? How to overcome it?
- What is boosting and bagging in machine learning?
- What kind of algorithm is used to build an Amazon-like system of recommendations (“What other items do customers buy after viewing this item?”)?
- When is Ridge regression considered better than Lasso regression?
- What is a convex hull?
- What is regularization? When it might be helpful?
- How would you approach building a model that predicts to whom a user will send an email to?
Leverage your data
When you hire data scientists, you take the first step to improving decision-making and innovating your business. The level of this improvement can be even higher if you decide to build an even broader data team, one that includes experts like AI developers, data engineers, or GAN networks experts.
Whatever type of tech talent you need, Ideamotive can supply. We run a leading network of IT professionals looking for new career opportunities. From ML and AI experts to web developers and IT project managers, we can connect you with the right people based on your company profile, product specification, and any other requirements you might have.
Get in touch with us today to build a tailored team of IT experts.
