Data Scientists

Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. May create dynamic data reports.

The occupation "Data Scientists" has an automation risk of 59.4%, indicating a moderate probability that many aspects of the role can be replaced or augmented by automation technologies. Much of this risk arises from the structured and repeatable nature of several data science tasks, which are highly compatible with advancements in machine learning, artificial intelligence, and data processing automation. For instance, recommending data-driven solutions to key stakeholders—one of the most automatable tasks—can now be partially achieved via advanced analytics dashboards and report generation tools, which automatically analyze data and suggest actions. Similarly, analyzing, manipulating, or processing large datasets using statistical software is increasingly being automated by platforms that feature built-in scripting, automatic data cleaning, and easy integration of machine learning models. Applying feature selection algorithms to models is also a task well-suited for automation, as algorithms have been developed to efficiently and consistently identify the most relevant variables, reducing the need for manual human intervention. Despite these trends, certain components of the data scientist’s role remain resistant to complete automation. For example, writing new functions or applications in programming languages to conduct analyses requires both creativity and a deep understanding of the data’s context, tasks that current automation tools struggle to perform without explicit human guidance. Testing, validating, and reformulating models to ensure accurate predictions requires nuanced judgment, critical interpretation, and often domain expertise—skills that AI has not yet mastered at the level of experienced data scientists. Interestingly, recommending data-driven solutions to key stakeholders also appears among the most resistant tasks, emphasizing that while the generation of recommendations can be automated, the effective communication and contextual tailoring of those solutions still largely depends on a human touch, especially in high-stakes or sensitive decision environments. A deeper look at the occupation reveals several key bottleneck skills that protect data scientists from full automation. At high and medium skill levels, these include advanced programming capabilities, sophisticated modeling and statistical analysis techniques, and domain-specific knowledge. Data scientists must possess not only technical proficiency but also critical thinking and problem-solving skills to interpret results, adjust algorithms, and ensure methods are both valid and ethical. Communication skills are especially vital for translating quantitative findings into actionable business strategies and tailoring presentations to diverse stakeholders. As automation tools continue to evolve, data scientists who cultivate these complex, integrative abilities will be better positioned to adapt and thrive, serving as indispensable links between advanced technology and strategic business objectives.