Data Scientist

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Recruiting for the Data Scientist position?

Are you starting your recruiting process for the position of Data Scientist in your company and wondering how to start creating a job offer? We’re here to help. We have prepared a small cheat sheet for you below, which will help you understand what Data Scientist is responsible for. Once you have got to know it, feel free to use our job offer template as a whole or as an inspiration in your recruitment.

What Data Scientist does?

The Data Scientist looks at what questions need to be answered and sees where it can find the right data to analyze and identify those answers. He has very good analytical skills and business understanding, as well as the ability to search, extract and analyze large amounts of unstructured data. They can often end up developing predictive models to theorize and forecast certain aspects.

Hiring for Data Scientist role?

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Role in the organization:

We are looking for a Data Scientist to join our team, a specialist in data analysis. The person in this position will be responsible for analyzing large datasets, building predictive models, and extracting valuable insights from data. If you are passionate about data analysis, can draw accurate conclusions based on data, and want to develop in this area, join our team. Please read the job offer and expectations for candidates below.

Our requirements

Professional experience in data analysis
Knowledge of data analysis tools and techniques such as machine learning, data mining, and statistical analysis
Programming skills in languages such as Python, R, SQL
Familiarity with data analysis tools and frameworks such as TensorFlow, PyTorch, and scikit-learn
Good understanding of data processing and databases
Analytical thinking and the ability to communicate analysis results in a understandable way for non-technical people
Higher education related to data analysis, computer science, mathematics, or related fields
Very good command of the English language

Your responsibilities

Data analysis and building predictive models
Conducting experiments and testing various models
Optimizing existing models for performance and accuracy
Preparing reports and presentations based on analyzed data
Collaborating with programmers and data analysts
Monitoring and evaluating results and continuously improving analytical processes