Data-driven decision making can dramatically affect an organization’s growth and success. As a result, the demand for data scientists is skyrocketing. Read on to find out which industries are hiring the most data scientists.
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If we think of big data as containing troves of market insights, consumer trends, and answers to some of the most pressing problems facing organizations, then it helps to also think of data scientists as holders of the keys to unlocking those valuable insights.
Big data alone doesn’t present game-changing insights upfront—in fact, when left unsorted, it is messy, overwhelming, and largely unusable. Data scientists must collect, organize, and analyze vast data sets in order to extract useful information. Big data presents growth opportunities to organizations in all fields, but certain industries are leading the charge in putting their data to work.
The fields of finance, professional services, and information technology employ the most data scientists.
The finance industry, which includes banks, investment firms, insurance firms, and the real estate sector, uses data science to calculate risk, detect fraud, and predict market activity. In this context, data science is used to protect an organization’s bottom line. Financial institutions from big banks to insurance providers use data to prevent losses by flagging unprofitable customers, bad deals, and security breaches or scams. Data science is also used to automate trading and assess risks tied to major transactions.
The professional services industry uses data science to help businesses optimize their operations. Data scientists working in the professional services industry assist clients in the collection, management, and analysis of data. Data scientists collaborate closely with clients to interpret findings and create actionable insights that will facilitate growth.
The technology industry, which encompasses any number of app and digital platform makers and service providers, uses data as its primary driver of product development. Machine learning lies at the heart of nearly every social media product, and key tech industry players are constantly adapting algorithms and AI to improve user experience and, in turn, collect more consumer data.
The goal of any data scientist is to tease out meaningful and actionable insights from collected data. But a data scientist’s specific function and areas of focus can vary greatly depending on the needs of their industry.
The world of finance uses data science to reduce losses and augment profits. In the financial industry, data scientists use predictive modeling to forecast consumer behavior and mitigate potentially costly impacts. Predictive modeling is also used to forecast the behavior of financial markets.
The financial industry also relies on machine learning to detect fraud and conduct trade surveillance. Using neural networks and memory models, data scientists create algorithms that identify anomalies in customer behavior that indicate identity theft and credit card scams. Algorithms also reveal anomalies that point to insider trading: a sharp increase in the volume and frequency of transactions after an internal dissemination of sensitive information can suggest that foul play is afoot. Hedge funds and investment banks also use machine learning algorithms to automate market trading.
The professional services industry provides external support to businesses, often in the form of legal services, tax advisory, database management, or data-driven consulting. Data science is used to optimize operations across a variety of industries. Law firms can use analytics to guide their legal approach by identifying ruling patterns in prior cases. Ad agencies use data to create targeted marketing campaigns and improve their search engine rankings through analytics-based strategies like SEO and SEM. USG, a leading materials manufacturer, uses predictive analytics to streamline production.
Professional services firms use data science to help organizations optimize their operations. The process begins with helping the client identify business problems that big data can solve. Next, the firm will work with the client to determine new data sources and outline ways for the client to capitalize on data that the organization already has.
The professional services firm will then design and implement databases and storage architectures on the client’s behalf. The firm will manage, prepare, and analyze data, and present findings that will help the client improve performance and profitability.
Big data powers the world of tech more so than any other industry, from optimizing marketing capabilities to improving user experiences. Social media platforms, for example, deploy deep learning to improve targeted advertising. Facebook, in particular, uses neural networks to recognize faces in photos and has developed a feature called Deep Text to analyze written communications. These machine learning features shed light on user interests and behavior, which in turn informs the ads they see.
In the realm of e-commerce, companies such as Amazon harnesses predictive analytics to recommend products to customers. The insights derived from predictive analytics enable Amazon to run an anticipatory shipping model in which a customer’s purchase patterns dictate where certain products are physically stored. If analytics suggests that a customer will buy a certain product, it is preemptively stocked in a nearby warehouse.
Tech firms rely on big data to track and forecast trends in user behavior. Across the board, tech companies use data-driven insights to understand what users want and create solutions that meet those needs. Furthermore, data is the foundation of AI, which powers many of the top products and services that tech companies provide.
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