What is a Data Scientist?

Similar Job Titles:

Data Architect, Data Engineer, Business Intelligence Specialist, Research Analyst

Data Scientists are responsible for collecting, organizing, and analyzing large data sets to provide key performance metrics and insights to their organization. These individuals require an extensive amount of data to develop key hypotheses; using analytics and reporting tools to detect specific trends, patterns, and relationships within these data sets. 

What does a Data Scientist typically do? 

Leveraging business data 

Data science is complex and involves taking large amounts of information from a variety of major technological fields; it takes into account the overall big picture more than any other analytical field. Data Scientists are very knowledgeable about specific technological fields and incorporate their values within their research. Data scientists will collect and clean data by creating algorithms, discovering patterns & trends, and performing experiments. Their overall goal is to leverage this data to find solutions hidden within large data sets to improve overall business performance.

Helping brands understand their consumers

Once a Data scientist completes their analysis’, they learn more about trends and patterns within campaigns and consumers. Data scientists will make predictions based on consumer behavior and marketing effectiveness to help their company create a strong business plan to engage with their audience. Overall, increasing products sold and services used by consumers; improving the long-term success of a business.

Knowledgeable of major technological fields & platforms

A data scientist must use their strategic and analytical mindset to incorporate mathematics, statistics, economics, and computer science within their research. They also have a wide range of technical competencies including machine learning, coding languages, and reporting technologies. They are familiar with data platforms and tools such as Hadoop, Pig, Hive, Spark, and MapReduce as well as programming languages that include SQL, Python, Scala, and Perl; statistical computing languages, such as R.

Data Scientist vs. Data Analyst

A data scientist is a professional who utilizes data collections to discover patterns and trends in order to make future predictions to improve business processes. Data Analysts, on the other hand, will shift through data to create visualizations and translate information that was discovered; as they do not make predictions based upon their findings. 

Important metrics of Data Scientist

  • Accuracy
  • Precision
  • Recall
  • F1 score
  • Log Loss/Binary Crossentropy
  • Categorical Crossentropy
  • AUC

Data Scientist Salary

US Based, employer-reported data for a Data Scientist:

  • 25th Percentile $63.4K
  • Average $69.2K
  • 75th Percentile $74.5K
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Data Scientist Job Description

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Copy the following text and paste it into your own job description, or, into our downloadable template in the section that looks like this: <INSERT JOB DESCRIPTION COPY FROM BLOG POST HERE>

As a Data Scientist at <CompanyXXX>, you’ll not only discover impactful information within large data sets, but you will develop a deep understanding of data’s potential to drive business solutions. More specifically, you’ll get to:

  • Analyze and clean data for accuracy while searching for patterns and trends
  • Use models to develop predictions to improve customer experience and revenue generation
  • Execute experiments carefully to help solve various problems in order to make true improvements
  • Implement specific techniques and tools to monitor model performance
  • Communicate and translate strategic solutions to key decision-makers
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