What Are the Responsibilities of a Data Scientist?

Data scientist roles and responsibilities include collecting, cleaning, validating, analyzing, and interpreting data. Learn more about each in this guide.

Businesses and organizations are constantly collecting data, from web analytics and digital ad performance to user behavior, and more. A data scientist’s job is to identify data analytics problems that offer the greatest opportunities to the business or organization.

Learn more about how to become a data scientist here.

5 Key Responsibilities of a Data Scientist

The main responsibilities of a data scientist center around collecting, cleaning, validating, analyzing, and interpreting data.

  1. Collect. The first step toward establishing an active data analytics platform is to collect structured and unstructured data from different sources. Unstructured data consists of things like what customers are saying about the organization on social media, while structured data is a measurable metric, such as customer lifetime value.
  2. Clean. A data scientist then cleans and validates the data to ensure its accuracy and completeness. While AI-powered data analytics tools can automate part of this process, cleaning data still makes up the bulk of a data scientist’s job duties.
  3. Analyze. Once the data is clean, the data scientist analyzes the data to identify patterns and trends using advanced statistics skills.
  4. Interpret. The most crucial part of their job is to interpret the data and discover solutions and opportunities for the business.
  5. Communicate. Finally, the data scientist communicates these findings to stakeholders using various means, such as data visualizations, dashboards, and reports.

Learn more about each of these key data scientist responsibilities below.

data scientist responsibilities

Problem Definition

The problem statement is the most crucial step towards solving any data analytics problem. This is usually defined by the organization. For example: "We need to increase sales of product X in category Y."

As a data scientist, you need to think of the problem statement in mathematical terms. For example: Why is product X underperforming? What data on user behavior might help explain it?

This is where pain points come in. These are areas where the business is struggling. Some are obvious; others may be undiscovered until further on in your data analysis.

Before writing the problem statement, a data scientist needs to consider the following:

  • What problem is the organization trying to solve?
  • What impact does this problem have on the organization?
  • What are the potential benefits of solving this problem?

The problem statement generally follows the format:

"The problem P, has the impact I, which affects B, so a good starting point would be S."

P = The problem

I = The pain points the organization is facing

B = Which parties are affected by the problem (eg: customers, suppliers, IT)

S = The proposed course of action

The next step is to translate business goals into data analysis goals so you can determine a course of action. Decide if the expected benefits are realistic and attainable from a data standpoint; for example, how long will the project take? Will you need additional data sources? Is the existing dataset accurate and adequate?

Data Collection and Wrangling

Data wrangling is the process of cleaning, restructuring, and enriching raw data to make it easier to analyze. The primary goal of data wrangling is to reveal a “deeper intelligence” by gathering data from multiple sources and organizing the data for a broader analysis.

  • Discover. In this stage, the data scientist’s job is to understand the data more deeply so they can decide how to clean and organize it.
  • Structure. The data needs to be restructured in a way that best suits the analytical method used. Based on the criteria established in step one, the data will need to be separated and reorganized for ease of use (similar to tidying up an Excel spreadsheet).
  • Clean. Duplicates, outliers, and null values must be eliminated or changed, and the formatting of the data should be standardized (for example, units of measurement or currencies should be the same so comparisons can be made).
  • Enrich. Take stock of what is in the data and decide whether you’ll need additional data to make it better, or if you can derive anything new from the clean dataset you already have.
  • Validate. This is where data scientists need to know a little bit about programming. Validation (a.k.a. data integrity validation) refers to repetitive programming steps used to verify the consistency, quality, and security of the dataset. Without validating the data, you risk basing important business decisions on imperfect data.

Learn more about data wrangling here.

Exploratory Analysis

Now that the data is clean, it’s time to probe it for insights. Exploratory data analysis postpones any initial assumptions, hypotheses or data models; instead, data scientists seek to uncover the underlying structure of the data, extract important variables and detect outliers and anomalies.

Most of this work is done graphically because graphs are the easiest way to visually infer trends, anomalies, and correlations, aside from natural pattern recognition just by eyeballing the data. You might tinker with different types of visuals, such as histograms, box plots, probability plots, or even bar charts.

Find out how to use exploratory analysis using Python in this video!

Data Processing

The data processing cycle refers to the set of operations used to transform the data into useful information. Graphs, documents, and dashboards can be interpreted by computers and used by employees.

In this stage, the data is entered into a system, such as a CRM like Salesforce or a data warehouse like Redshift so that a data processing cycle can be established. In the next stage, this process is deployed as a repeatable data model to enable long-term data analytics projects.

Remember, the job of a data scientist isn’t to produce a single report or static dashboard. Rather, it’s about establishing a long-term strategy for collecting, analyzing, and acting upon data.

Model Training and Deployment

Data modeling represents the way data flows through a software application or the data architecture within an enterprise. Think of it as a blueprint that establishes relationships between different data objects and how they relate to one another.

Data scientists achieve this by splitting the data into a training set and a test set.

  • The training set is used to fit and tune algorithms (i.e. the data is analyzed for trends and patterns that can be used to create an algorithm for predicting future, “unseen” data)
  • The test set is the other half of the data that is left untouched until the very end, so that it can be evaluated against the algorithm

Data scientists can’t use the same data for both the test set and the training set because the data model will be overfit. Data model overfitting occurs when a function is too closely fit to a limited set of data points, resulting in a somewhat biased algorithm that does a poor job of predicting new data. To properly tune the model, data scientists perform cross-validation. This is a way of estimating the performance of the model using only the training data.

  • To do this, data scientists split the data into 10 equal parts or “folds”
  • The data model is trained on the first nine folds, then evaluated against the one remaining “fold”
  • This is done ten times, each time holding out a different fold
  • Finally, data scientists average the performance across all 10 holdout folds
  • The resulting number is your final performance estimate, also known as your cross-validated score

Finally, data scientists use model deployment. This refers to the process of integrating the data model into an existing production environment. This means the algorithm is ready to take new inputs and return outputs that can be used to make practical business decisions.

Documentation, Visualization, and Presentation

Just like in the software engineering process, data scientists are expected to document their processes, providing sufficient descriptive information about their data for their own use as well as their colleagues and other data scientists in the future. Documentation is known as metadata since it concerns data about data.

Proper documentation consists of methodology, information on data processing, a list of variables in the data, file formats (FITS, SPSS, HTML, JPEG, and any software required to read the data), as well as access info (where and how your data can be accessed).

Data visualization is perhaps the most crucial aspect of the data function—computational statistics are only meaningful if they can be understood and acted upon by the organization. Successful data scientists understand how to create narratives with data.

Reporting is a central facet of data science outcomes and the dashboard is a default reporting tool. However, data scientists also use charts, graphs, and reports to communicate their findings to a range of stakeholders. Those with advanced coding skills are also known to use animated charts and interactive visuals to model different scenarios.

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