Vp Of Business Intelligence Salary
Vp Of Business Intelligence Salary – Data science is a field that encompasses several different disciplines. But all of these data science roles require strong analytical skills, technical knowledge, and the ability to gather actionable insights from troves of information.
As more and more industries use innovative data applications-from the health sector using data science to improve patient care and drug development to Spotify using music data for personalized recommendations-data professionals of all stripes are in high demand. And there are a variety of job opportunities to suit a variety of passions and interests.
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Data science professions command high salaries because they are in short supply (the profession has nearly 20,000 open positions in the United States, according to US News) and their skills are in high demand.
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Data scientists are seeing this demand because, in the past few years, many organizations have begun to recognize the importance of data-driven decision making. Whether it’s an e-commerce company building artificial intelligence or a travel company integrating machine learning into its platform, data scientists are at the forefront of the latest technology revolution.
In the United States, data science professionals typically earn around $117,000 per year, according to Glassdoor. However, this can vary depending on a number of factors, including years of experience, education level, industry, location, and area of specialization. For example, in big cities like New York or San Francisco, a data scientist can earn up to $180,000. Machine learning engineers often earn more than data analysts, and those working in the consumer technology sector typically earn more than those working for government agencies, nonprofits, or healthcare organizations.
Data scientist responsibilities can vary from company to company. In general, data scientists source and process data available to companies.
The average annual salary for a data scientist in the United States is $117,000, according to Glassdoor.
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Data engineers build pipelines that feed data from various sources. They focus on data production readiness and raw elements such as format, robustness, scale, data storage, and security.
The average annual salary for a data engineer in the United States is $113,000, according to Glassdoor.
A data architect describes and designs an organization’s data management framework. They do this by evaluating the organization’s data sources, then designing a plan to consolidate, centralize, and maintain the data they’ve collected.
– Application server software such as Oracle, user interface and query software such as IBM DB2, and database management software such as Microsoft SQL Server
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The average annual salary for a data architect in the United States is $152,000, according to Glassdoor.
Data modelers translate real-world business needs into data models. They work with data architects to create conceptual, logical, and physical data models. He supports the application team when designing the database and collaborates with the data governance team for compliance.
The average annual salary for a data modeler in the United States is $106,000, according to Glassdoor.
Big data engineering focuses on the infrastructure that enables people to collect and manage large amounts of data. It makes billions of clicks, taps, likes, swipes, shares, and purchases work for data analysis. They do this by building data pipelines, designing and managing data infrastructure, handling data storage, and focusing on ETL (Extract, Transform, Load) processes.
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The average annual salary for a big data engineer in the United States is about $125,000, according to Glassdoor.
Machine learning (ML) engineers are programmers who design automated software that uses data and automates predictive models. ML engineers bridge the gap between data and software, creating programs that allow machines to function without direct human assistance.
The average annual salary for a machine learning engineer in the United States is $123,000, according to Glassdoor.
Machine Learning is a subset of data science. Although machine learning engineers and AI engineers perform some of the same tasks, artificial intelligence (AI) engineering places greater emphasis on the development of systems and machines that mimic human cognitive functions, making them more adept at problem solving.
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The average annual salary for an AI engineer in the United States is $118,000, according to Glassdoor.
An enterprise architect is an IT professional who oversees an organization’s network and services. They update company-wide systems, replace hardware or software, and determine the best products and services for an organization’s support team.
The average annual salary for a corporate architect in the United States is $164,000, according to Glassdoor.
Business Intelligence (BI) analysts use data to make decisions. They collaborate with business teams to understand needs, review data, and write financial reports and market intelligence.
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Natural language processing engineers build systems and devices that can understand human language. A subset of artificial intelligence and machine learning, NLP engineers work at the intersection of computer science, information science, AI, and linguistics.
The average annual salary for an NLP engineer in the United States is $88,000, according to Glassdoor.
Database managers are often confused with database administrators, but their roles are different. While the former creates, updates, and deletes the database, the database manager maintains the results and performance of the database. The database manager also maintains the security and safety of the organization’s database.
The average annual salary for a database manager in the United States is $76,000, according to Glassdoor.
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The average annual salary for a database developer in the United States is $97,000, according to Glassdoor.
A stepping stone to a data science career, data analysts identify trends, patterns, and relationships in data to inform business decisions. Data analysts mine data, organize insightful findings, build visualizations, and report reports to stakeholders.
– Data cleaning to rid data of errors and discrepancies, and gather useful insights through the identification of patterns, relationships, and trends
– Familiarity with data analytics tools such as Tableau, Google Analytics, Jupyter, Amazon Web Services, and Microsoft Excel
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The average annual salary for a data analyst in the United States is $74,000, according to Glassdoor.
The more companies rely on data, the more likely they are to pay data scientists. Some of the highest paying data science companies are social networking sites (which collect user data), e-commerce platforms (which track billions of data points on consumer behavior), and real-time services (which rely on data to be responsive to customers ).
Amazon’s data scientists work across all verticals, from parsing consumer purchase data on e-commerce platforms, to analyzing user behavior for advertising divisions.
The average base salary for an Amazon data scientist is $137,000, according to Glassdoor, although Amazon employees often make more. By 2022, Amazon is raising the base salary cap to $350,000 for corporate and technology employees, citing competition for tech talent.
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Uber data scientists work in product analysis (for example, ensuring the accuracy of maps and geolocation technology), finance and operations (for example, using data to help companies with financial planning and competitive strategy), and marketing (for example, drawing analytical insights to help increase growth, brand awareness, customer retention, and engagement).
Meta data scientists work on products such as Facebook, Instagram, Oculus, Messenger, and WhatsApp. A data scientist focused on Instagram can help the monetization division drive long-term strategies to show results. Business analysts at WhatsApp may collaborate with sales and marketing teams to identify opportunities for user growth. And machine learning engineers at Facebook may be working on fraud detection technology.
The average base salary for a data scientist at Meta is $152,000, according to Glassdoor. As one of the highest paying tech companies in the world, it is not uncommon for data science professionals to make upwards of $200,000 at Meta.
Airbnb’s data scientists work on a variety of products and features, from fraud prevention to helping adjust rental prices. As companies grow, so does the demand for data science professionals. Airbnb currently has dozens of open roles for data scientists, machine learning engineers, and analysts.
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With more than 900 million active iPhones in the world, Apple is a data-gathering juggernaut. The company’s data science professionals work across all divisions, including hardware, software, music, and entertainment services.
Data scientists working for consulting firm Booz Allen experience a variety of clients and challenges, from helping commercial clients improve their cybersecurity, to assisting military clients.
The size, reach, and diversity of Microsoft products means that data scientists can solve a wide range of problems, from improving the Microsoft Cloud to preventing bias in language technology.
The number of tools data science professionals master can affect their compensation, according to the O’Reilly Data Science Survey. Some tools that increase salary include Apache Spark, Scala, and D3, which can add up to $15,000 to your compensation package. Expertise with cloud computing will also increase salary, with survey respondents using Amazon Elastic MapReduce earning around $6,000.
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O’Reilly also notes that those who use 15 or more tools can make up to $30,000 more than those who only use 10 to 14. There is a premium associated with mastering multiple tools.
Where you choose to work can determine your salary.
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