Searching for a Data Scientist (Machine Learning)

Studies show that, by 2025, the amount of data in the world will reach 180 zettabytes (1 zetabyte = 10²¹ bytes). It would take more than 180 million years just to download all the data from the network, and what about processing it?

Data scientists are the specialists who help businesses use data and get predictions and insights based on huge amounts of information. The big data analytics market is estimated at $307.52 billion.

Do you want to maximize the potential of your product’s data and leverage trends to your advantage? To do this, you need an experienced data scientist. Keep reading if you’d like to find out about who this person is, what requirements should be specified in the vacancy, and how to find such a candidate.

What data scientists do: positions and specializations in data science

Data scientist — who is it? Let’s look at the different positions in data science. They can be divided into four groups:

• data collection and storage: data engineer, data architect, and other specialists;
• data processing: data analyst and data researcher;
• collecting, storing, and processing large amounts of information: big data engineer / Spark engineer;
• decision-making / forecasting systems, including those with AI elements: machine learning engineers, deep learning engineers / data scientists, and data science engineers.

Due to the multitude of areas, domains, and different technologies in the field, it is easy to confuse one position with another. In addition, specialists often switch between related areas.

Data scientist specialists — what do they do, and what do companies expect from them? Most often, when hiring such professionals, businesses have the following needs:

• identifying valuable data sources and automating the process of collecting them;
• processing structured and unstructured data;
• analyzing large amounts of information — searching for trends and patterns;
• building predictive models;
• data visualization;
• formulating recommendations and strategies for solving business tasks using the obtained data;
• working in a development team and interacting with stakeholders.

In addition, when companies put together a portrait of an ideal candidate, they list the ability to work with machine learning as one of the desired skills. What is it? It’s the use of techniques to teach programs to perform certain tasks, from finding a cat in pictures to analyzing online demand for millions of customers. Creating ML algorithms and models and using machine learning to develop software products is exactly what a machine learning engineer does on a project.

💡 Interesting to know: there are also supporting positions in the field that facilitate the work of a data scientist. Who are they? These can be such positions as database administrators, who help data engineers configure databases, as well as MLOps, who automate the infrastructure used.

What skills should a data scientist have?

The requirements for data scientists differ from project to project, but the following items can be found in a standard job posting:

• basic knowledge of Python;
• experience using SQL or other data query languages (e.g., MapReduce);
• knowledge of data cleaning and dataset optimization techniques;
• advanced knowledge of at least one data processing tool or framework;
• strong English language skills;
• nice-to-have: higher technical education (depends on the company).

In addition to the standard requirements for years of experience and domain expertise, data scientist job postings often include requirements for proficiency in the following technologies: Databricks, NumPy, SciPy, Pandas — popular libraries for data analytics. In some projects, data scientist skills should cover deep learning frameworks (Tensorflow, PyTorch) and clouds (AWS, GPC, or Azure).

Above, we’ve already explained what a machine learning engineer does. Such positions are not easy to fill: you need to analyze many nuances. For example, vacancies in machine learning include requirements for experience in computer vision (CV), natural language processing (NLP), predictive analytics (PA), and other fields.

Mykola Kliestov,

CTO at ITExpert

“If you want to hire an experienced ML engineer, it is important to understand what skills you want to see in your candidate and share them in the job brief.

The recruiter should analyze experience in certain domains not only by the candidate’s previous place of work but also by keywords — the technologies used. For example, proficiency in Bert or Word2Vec can indicate experience in NLP, and knowledge of R development language — in fintech. If your project requires a candidate with experience in deep learning, proficiency in neural networks (e.g., GAN or CNN) will set them apart from others.

The key difficulty is that you may get a lot of irrelevant applications on standard platforms for finding IT professionals. That’s why, when hiring a data scientist, you need to make targeted offers, conduct in-depth candidate screening, or seek help from IT recruitment agencies to fill the position on time.”

Searching for a skilled data scientist with the help of the ITExpert team

The specialists at IT recruitment agency ITExpert have been hiring IT specialists for companies around the world since 2015. Despite the high level of competition in the field, we were able to fill data science positions in just 2-3 weeks. Our clients include Sony, Deloitte, and Depositphotos.

Finding specialists with a rare or unique set of skills is our forte. Tell us about your vacancies, and we will find a data scientist suitable for your project!