What Are the Daily Tasks of a Data Scientist?
Published on July 29th, 2019
As companies in a variety of business sectors struggle to wean actionable information from their massive data stores, they increasingly rely on the data scientist to help them achieve this goal. This need created a strong demand for data scientists throughout the technology world. It’s a job role providing a great opportunity for interested technology professionals looking to change their career path.
If you are interested in becoming a data scientist, here is a quick overview of what the position entails. Ultimately, understanding the daily life of this important tech role helps you make an informed decision on your career.
Defining the Role of the Data Scientist
As noted above, a data scientist is charged with collecting, analyzing, and ultimately deriving insight from an organization’s business data. They leverage a host of modern technology innovations to find meaning within data. These include machine learning, predictive modeling and other AI-related tools.
A business engages data scientists for a variety of reasons. In some cases, predicting customer behavior is the goal. Other times, they want to identify business risks or new opportunities as part of the corporate planning process. Trying to get a leg up on their competition is typically the underlying reason for using data science in this manner.
Responsibilities and Duties of a Data Scientist
A typical day for a data scientist includes building predictive models based on statistical data. This requires familiarity with a wide range of statistical analysis tools as well as experience actually analyzing and constructing predictive models themselves. Increasingly, writing machine learning algorithms is part of this process, requiring data scientists to actually code, usually in the Python language.
The data scientist is responsible for technical deliverables – typically machine language algorithms, predictive models and occasionally, data designs. They also possess the keen business acumen to help them find those actionable insights in the data. This combination of strong technical and business skills helps differentiate the data scientist from data analysts or DBAs.
A data scientist’s softer skills also come into play on a daily basis. These include a problem-solving ability that helps them write a ML algorithm able to find insights in an old customer database. Strong communication skills are also essential when interacting with both technical personnel and business stakeholders.
If you want the unique opportunity of a data scientist, reach out to your recruiter for help on crafting a career path to reach this goal.
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