In today's business landscape, nearly every aspect can be converted into data and scrutinized by specialists. The influence of data science is vast, impacting all business areas, including marketing, sales, HR, and software development, such as AI-driven systems like ChatGPT. No sector is immune from being quantified and influenced by data. Professionally constructed machine learning algorithms can assist medical patients in comprehending their health status or help bank customers examine their expenditures. However, businesses generally utilize data science for actions 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.
Furthermore, thanks to technological progress, we don't perceive data as merely a descriptor of the past or present. In reality, the real strength lies in how data can assist in future forecasting.
As data became a prevalent trend, businesses shifted their focus from 'why employ data scientists?' to 'how can we hire data scientists?'. We've already answered the first query — it's time to answer the second one, which is more crucial, as data scientists are now constantly in demand, and locating one that perfectly suits your business can be challenging.
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 company's current needs.
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?
To fully understand the role of data scientists, it's crucial to differentiate them from data analysts and data engineers. These three terms are sometimes used interchangeably, which is inappropriate.
Unlike data scientists, data analysts typically don't build machine learning or other sophisticated data-related models. They usually analyze data provided to them by others, often from a single source, to gain new insights into existing problems. On the other hand, data scientists use machine learning and other relevant technologies to predict the future. To do so, they incorporate data from multiple, often unrelated, sources into their work.
What about data engineers? These individuals mine data from various sources and construct pipelines to deliver this data to those who will later incorporate it into their research, such as data analysts or data scientists. If there are no dedicated data engineers at the company, data scientists may mine data themselves.
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.
