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2 Types of Data Scientists: 2 Sides of the Same Coin

Head of Data Analytics Department, ScienceSoft

3 min read

Editor’s note: Desperately searching for data science talent? ScienceSoft’s data science consultants are ready to help you both in word and deed. Read on to learn what types of data scientists we differentiate and get the required competence from us.

Let’s face reality: data science unicorns do not exist. If you are searching for a data scientist who can do anything from defining your data science-related business needs regardless of the field you operate in to building a complex deep neural network with the same mastery, you are at high risk of never finding such a person. In data science, specializations rule.

With that, you can find numerous approaches to classifying data scientists, which can include from 2 to more than 10 data science jobs/specializations. At ScienceSoft, we also distinguish among different types of data scientists having a professional data science team on board. Still, being committed to keeping things simple, we recognize just 2 types of data scientists: analysts and technicians. Let’s get to know their core responsibilities better.

types of data scientists

Data scientists – analysts

Data scientists – analysts are proficient in translating business needs into the design of data science solutions, as well as interpreting the findings achieved with the help of these solutions back to the business. To do this successfully, they should have a solid grasp of industries they serve, as well as domain knowledge like supply chain management, predictive maintenance, and quality management.

The core responsibilities of data scientists – analysts are:

  • Analyzing business needs that require data science, like forecasting, optimization, root cause analysis.
  • Managing the quality of raw data.
  • Preparing the data required to train a machine learning model (for example, augmenting data and reducing noise).
  • Defining factors that influence the accuracy of predictions (for example, defining that for the purpose of demand forecasting, a machine learning model should analyze latest sales trends, seasonality, patterns specific to each SKU, as well as the influence of promotions).
  • Exploring data and interpreting results (i.e., making sure that the model differentiates signals from noise).
  • Building reports and dashboards to visualize the analysis findings.

To get a real feel about the work this type of data scientists performs, check out one of the projects from ScienceSoft’s portfolio that illustrates their competence: Data Science for an Automated Trading System.

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Data scientists – technicians

Data scientists – technicians are adept at converting a data science concept into a robust solution. They work with mathematical formulas and code, making machine learning algorithms consume data and produce the relevant output. For example, in one of ScienceSoft’s latest data science blog posts, our Head of Data Analytics Department explained in detail how a deep learning model predicts the optimal inventory level based on historical sales data.

The core responsibilities of data scientists – technicians are:

  • Choosing the optimal machine learning algorithm among the available options.
  • Designing and implementing machine learning (including deep learning) models.
  • Choosing relevant activation and optimization functions.
  • Tuning the models’ hyperparameters.
  • Training and retraining the models.

Check a real-life project from ScienceSoft’s practice to see how our data scientists designed and implemented a convolutional neural network to enable automated medical diagnostics: Development of a Brain Tumor Localization Application.

No need to choose between the 2 types. Get both!

ScienceSoft has gathered a pool of data science professionals (both analysts and technicians) who are ready to drive and back up the improvements that your business longs for, no matter the area. With us, you’ll be able to increase your production efficiency and sales effectiveness, optimize your supply chain, predict customer behavior, and offer your customers with an impeccable experience. If you are still in doubt as to which type of data scientists your business needs, hesitate no more! ScienceSoft’s projects show that our customers needed the competence of both roles.

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Bringing data science on board is promising, yet difficult. We’ll solve all the challenges and let you enjoy the advantages that data science offers.