Artificial intelligence careers are in high demand. Artificial intelligence (AI) has created new opportunities during the past few years. It is creating waves throughout industries, making things that were previously inconceivable, like space exploration and melanoma diagnosis, possible. As a result, AI careers have also steadily increased; according to LinkedIn, AI professionals are among the “jobs on the rise.”

Don’t be scared of AI jargon; we have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of artificial intelligence. After data architect, cloud computing, data engineer jobs, and machine learning engineers it’s time for artificial intelligence careers in the Hot and on the Rise series.

Artificial intelligence career paths

Are you interested in artificial intelligence careers? Professionals with expertise in artificial intelligence are more needed than ever because it is a technology that is becoming more and more common. The good news is that there are many job options in the AI sector, so you can assume a variety of tasks and responsibilities depending on the position, your background, and your pursuits.

Nearly every sector has a need for skilled AI workers, including:

• Financial services
• Healthcare
• Technology
• Media
• Marketing
• Government and military
• National security
• IoT-enabled systems
• Agriculture
• Gaming
• Retail

Prospects of artificial intelligence

There are several artificial intelligence career options. The list that follows contains both AI-related employment and certain roles that collaborate closely with people in AI jobs:

Do you need a guide for these jobs? Don’t worry, we already covered you.

Artificial intelligence career guide

Despite being a young and specialized area, artificial intelligence occupations are diverse. There are many artificial intelligence career options, each requiring a unique set of qualifications.

Let’s examine some of the most wanted.

Data analyst

Long ago, a data analyst was someone who gathered, purified, processed, and examined data in order to draw conclusions. These were primarily routine, repetitive chores in the past. With the emergence of AI, most of the mundane work has been mechanized. The data analyst position has therefore been promoted to join the new class of AI occupations. Data analysts today compile data for machine learning models, then use the results to create insightful reports.

An AI data analyst, therefore, needs to be knowledgeable about more than simple spreadsheets. They must be knowledgeable about:

• To extract/process data, use SQL and other database languages.
• Python for analysis and cleaning.
• Dashboards for analytics and visualization software, such as Tableau, PowerBI, etc.
• Understanding the market and organizational context with business intelligence.

The typical compensation for a data analyst is $65,000. However, high-tech firms like Facebook, Google, and others pay over $100,000 for employment as data analysts.

Machine learning engineer

Software developers and data scientists come together to form the field of machine learning engineering. They use big data technologies and programming frameworks to develop data science models that are production-ready, scalable, and capable of handling terabytes of real-time data.

The ideal candidates for machine learning engineer positions have backgrounds in data science, applied research, and software engineering. Candidates for AI positions should have a solid background in mathematics, and familiarity with deep learning, neural networks, cloud applications, and Java, Python, and Scala programming. Understanding software development IDE tools like Eclipse and IntelliJ is also beneficial.

In the US, a machine learning engineer makes on average $1,31,000. Pay at companies like Apple, Facebook, Twitter, and similar ones is substantially higher, averaging between $170,000 and $200,000. Learn more about the pay for ML engineers here.

Machine learning vs artificial intelligence article explains the differences between them.

NLP engineer

Engineers in artificial intelligence (AI) with a focus on the spoken and written human language are known as natural language processing (NLP) specialists. NLP technology is used by engineers who work on voice assistants, speech recognition, document processing, etc. Organizations require a specific degree in computational linguistics for the position of an NLP engineer. They might also be open to hiring candidates who have a background in computer science, math, or statistics.

An NLP engineer would require expertise in sentiment analysis, n-grams, modeling, general statistical analysis, computer capabilities, data structures, modeling, and sentiment analysis, among other things. It might be advantageous to have prior knowledge of Python, ElasticSearch, web development, etc.

An NLP engineer has an average salary of $78,000, but with expertise, they can make over $100,000.

Data scientist

For a variety of goals, data scientists gather data, examine it, and draw conclusions. To extract knowledge from data and find significant patterns, they employ a variety of technological tools, procedures, and algorithms. This could be as simple as seeing anomalies in time-series data or as complicated as generating predictions about the future and giving advice. The following are the main requirements for a data scientist:

• Advanced degree in mathematics, computer science, statistics, etc.
• Statistical analysis and unstructured data comprehension.
• Having knowledge of platforms like Hadoop and Amazon S3 for the cloud.
• Expertise in programming languages like Python, Perl, Scala, and SQL.
• Working familiarity with Hadoop, Spark, MapReduce, Pig, and Hive.

A data scientist makes $105,000 on average. A director of data science role can earn up to $200,000 with experience.

Business intelligence developer

To find trends, business intelligence (BI) developers analyze intricate internal and external data. For instance, in a business that provides financial services, this could be someone who keeps track of stock market statistics to aid in investment selection. This might be someone who keeps an eye on sales patterns for a product company to help with distribution planning.

Business intelligence developers don’t really produce the reports, in contrast to a data analysts. For business users to use dashboards, they are often in charge of designing, modeling, and maintaining complex data on highly accessible cloud-based data systems. A BI developer is expected to possess the following skills:

• Engineering, computer science, or a related subject bachelor’s degree.
• Having practical knowledge of SQL, data mining, and other related topics.
• Knowledge of BI tools like Tableau, Power BI, etc.
• Powerful technical and analytical abilities.

Software engineer

For AI applications, software engineers create software. For AI jobs, they combine development activities such as creating code, continuous integration, quality control, API administration, etc. They create and manage the software used by architects and data scientists. They remain knowledgeable about current developments in artificial intelligence technology.

Software engineering and artificial intelligence expertise are prerequisites for an AI software engineer. In addition to statistical and analytical abilities, they must have programming skills. A bachelor’s degree in computer science, engineering, physics, mathematics, or statistics is often required by employers. Certifications in AI or data science might also help you get hired as an AI software developer.

Software engineers make $108,000 on average. Depending on your sector, specialization, and experience, this can reach $150,000.

Robotics engineer

When industrial robots began to gain popularity in the 1950s, the robotics engineer was possibly one of the first professions in artificial intelligence. Robotics has come a long way from the manufacturing lines to teaching English. Robotic-assisted surgery is used in healthcare. Robotic humans are being created to serve as personal assistants. All of this and more is what a robotics engineer does.

AI-powered robots are created and maintained by robotics engineers. Organizations often require graduate degrees in engineering, computer science, or a related field for these positions. Robotics engineers may be required to have knowledge of CAD/CAM, 2D/3D vision systems, the Internet of Things (IoT), as well as machine learning and AI.

Robotics engineers typically make $87,000 per year, but with experience and specialization, they can earn up to $130,000.

Big data engineer/architect

Big data engineers and architects create ecosystems that enable efficient communication between multiple business verticals and technology. As big data engineers and architects are often entrusted with planning, creating, and developing big data environments on Hadoop and Spark systems, this profession may feel more complicated than that of a data scientist.

Professionals with a Ph.D. in mathematics, computer science, or similar subjects are preferred by the majority of employers. However, because this position is more practical than, say, a research scientist, practical experience is frequently viewed as a strong replacement for a lack of academic degrees. Programming knowledge in C++, Java, Python, or Scala is required of big data engineers. Additionally, they must have knowledge of data migration, data visualization, and mining.

With an average compensation of $151,300, big data engineers are among the highest-paid positions in artificial intelligence.

Artificial intelligence job requirements

The traits that enable the most successful AI professionals to excel and develop in their jobs are frequently shared by these individuals. Working with artificial intelligence demands the capacity to think analytically and to come up with economical, efficient solutions to challenges. It also calls for insight into technological advancements that result in cutting-edge software that keeps organizations competitive.

AI experts also require technical expertise to create, maintain, and fix software and hardware. Finally, in order to do their jobs effectively, AI experts need to learn how to convey highly technical knowledge to non-technical audiences. This necessitates effective teamwork skills and effective communication.

Artificial intelligence education requirements

The majority of artificial intelligence programs are built on foundational computer science and math knowledge. A bachelor’s degree is required for entry-level work, whereas master’s and doctoral degrees are usually required for jobs requiring supervision, leadership, or administrative responsibilities.

Typical curriculum includes research into:

• Numerous math topics, such as probability, statistics, algebra, calculus, logic, and algorithms are covered.
• Neural nets or Bayesian networking are two examples of graphic modeling.
• Engineering, robotics, and physics.
• Coding, programming languages, and computer science.
• Cognitive science theory.

Candidates can search for degree programs with particular AI majors or pursue an AI emphasis within other majors like computer science, engineering, health informatics, graphic design, information technology, or engineering.

Artificial intelligence career salary

Jobs in artificial intelligence are in extremely high demand, and many of them pay well into the six figures. The precise figures will vary on a variety of elements, including the particular work responsibilities, industry, experience, level of education, and location.

However, this is a common range: A research engineer will make about $92,938 annually, according to Indeed, while a machine learning engineer would make about $150,183.

An artificial intelligence programmer typically earns between $100,000 and $150,000 each year, claims Datamation. On the other side, AI engineers make an average salary of $171,715 with top earners making over $250,000.

High pay are a result of the requirement for higher degrees and a rare mix of abilities.

Is AI a good career?

With a 31.4 percent growth in opportunities for data scientists and mathematical scientists, who are essential to AI, by 2030, the field of artificial intelligence has a bright future for career advancement.

The IT revolution is centered on artificial intelligence, which is constantly improving. AI is the driving force behind computer vision, speech analysis, and natural language processing. AI has a significant impact on business and society and will do so for a very long time.

The abundance of career prospects in the AI field is therefore not surprising; in fact, there are so many of them that the industry currently faces a unique problem: there are too many open positions and not enough competent applicants. The good news is that it provides nearly assured (and well-paying) work for those who are qualified.

Is AI difficult to learn?

Is AI difficult to learn? Yes, it can be, and 93 percent of automation technologists themselves feel underprepared for impending problems in the field of smart machine technology.

Artificial intelligence implementation presents various difficulties for businesses. Lack of personnel skills ranks as the biggest problem among them, affecting 56 percent of the businesses. Since AI is inherently difficult, it seems sense that most businesses feel this way.

It can be challenging to learn because of reasons like:

• Extensive programming: Programming is essential for AI. To teach computers to make their own decisions, you must learn how to code.
• Data proficiency: For machines to become skilled at an activity, they require a lot of data to learn from. Especially if you’re just getting started, getting this can be challenging.
• Complexity: Understanding AI requires knowledge of many disciplines, including computer science, statistics, calculus, and more.
• Lack of adequate tools: The majority of artificial intelligence tools and procedures in use today were created for conventional software. Newcomers to the sector frequently have to invest time and money in creating new tools, which can be challenging and time-consuming.

These figures do not, however, imply that there are no entry-level positions available in the field of AI and ML. Such employment opportunities abound, and you may get ready for them.

How long does it take to learn AI?

Although there is no denying that artificial intelligence’s future is bright, many people wonder how to get started in the field and how long it would take to master it. There is no clear answer to this query. In actuality, a variety of things have a role. But if you want the truth, studies have shown that it takes 10,000 hours to become an expert at any craft. Therefore, you could say that this also applies to machine learning.

Advanced ideas like deep learning, reinforcement learning, and unsupervised machine learning could require more time to learn. The length of the curriculum also affects how long it will take you to learn the skill because the majority of people who study artificial intelligence complete a certification program or course.

Conclusion

Artificial intelligence has advanced and improved people’s quality of life ever since its invention in the 1950s and continues to do so now in a variety of industrial contexts. As a result, an artificial intelligence profession will be fulfilling and sustainable for people who possess the ability to convert digital informational snippets into meaningful human experiences.

The majority of the current technology occupations are not careers in AI. Since AI is a rapidly developing discipline, experts working in the field must continually update themselves and keep up with new developments. AI/ML experts must regularly follow the most recent research and comprehend new algorithms; it is no longer adequate to just acquire abilities.

AI-related work opportunities are growing across a range of industries and are exciting and well-paying.

Additionally, AI is the subject of intense social and governmental scrutiny. AI experts need to consider AI’s social, cultural, political, and economic effects in addition to its technical components.

If you are asking “is artificial intelligence better than human intelligence“, we already have the answer. From the precursors of artificial intelligence to today, it is evolving and opening new opportunities for humanity. Artificial intelligence in developing countries is a suitable example of it. But can this one-sided interest change one day?

The demand for blockchain in HR is increasing, and we are here to explain why. Blockchain technology is quickly becoming a staple in many industries, including HR. Here are some of the advantages of using blockchain in HR, as well as examples of startups that are already using it. Blockchain technology can help with data management and other human resource activities. Certain HR areas are already adopting blockchain-inspired solutions. Rather than cryptocurrencies, there are several blockchain use cases, such as blockchain gaming. You will find the one most appropriate for your needs among the 4 types of blockchain in the best blockchain platforms as we saw it in enterprise blockchain examples.

Before we get started, here is a list of the best blockchain books in 2022 for better understanding. You may have heard about the blockchain talent gap and started to ask what is a blockchain developer. But unfortunately, you find some blockchain implementation challenges and security issues. Let’s take a closer look at whether this also applies to human resources.

Blockchain in HR

According to Gartner, blockchain will generate $3.1 trillion in business value by 2030. However, a significant proportion of these profits will be generated and efficiency enhancements in existing operational models and business processes. The true value will come from how it allows for a paradigm change in how societies, businesses, consumers, partners, and individuals interact, create, and exchange value.

In its most basic form, blockchain allows people who don’t know each other to exchange value in digital scenarios. In essence, blockchain provides trust in untrustworthy situations without needing a trusted central authority.

Blockchain technology is most recognized for its use in protecting the cryptocurrency infrastructure (e.g., Bitcoin) and ensuring secure financial transactions without the help of a bank or middleman. The technology, on the other hand, examines a landing in the human resources industry, which will undoubtedly affect how HR professionals handle huge quantities of personal data and apply various HR procedures. We already have several HR issues in which verifying identity and qualifications could be improved by using blockchain technology.

The rise of new HR technology has created a $10 billion global demand for HR software by 2022.

Deel, Bitwage, Papaya Global, and Chrono.Tech are a few of the firms that provide adaptive technology to manage employees while diversifying income sources so they may be kept and motivated.

How blockchain technology could impact HR and the world of work?

How can blockchain increase human resource management efficiencies? The consequences of the blockchain on HR aren’t just for businesses since blockchain transactions affect individuals as well as large corporations.

The use of the blockchain in HR has several benefits, including:

Security

In addition to managing sensitive employee data, HR teams are responsible for executing some of the highest-volume financial transactions for an organization as well as handling sensitive employee data related to pay, healthcare, finance, banking, disciplinary records, performance records, expense reimbursement, and more.

All HR department’s data is vulnerable to exploitation, and as more businesses have data breaches, precautions must be in place to prevent fraud and maintain security. Blockchain technology is being praised as a solution to increasing cybersecurity crime.  

Blockchain technology can aid in the prevention of both internal fraud and external attacks on sensitive employee records. The blockchain is restricted and controlled, with even those who have access unable to make arbitrary changes. This restricts both internal fraud and external hacks of important personnel records.  

Recruitment

Recruiting is a time-consuming and resource-intensive process for the HR department, to the point that businesses turn to third-party agencies or recruiters to reclaim lost time. However, owing to their high cost, such solutions may be counter-productive.

Most of the candidate information often discovered during the recruitment process may already be found on the blockchain. A considerable amount of the procedure has already been automated. Resumes will no longer be necessary, and looking at qualifications, certificates, work history, and experience should be straightforward.

Blockchain’s potential to eliminate many of the third-party and back-office components of recruitment may make recruiters obsolete.

Payroll

Multinational businesses may take steps toward adopting blockchain by developing their blockchain-based corporate currencies or “coins” that they can use to move value across their organization globally and transact with their supply chains. Central banks will also get involved, offering their blockchain-based means of exchange for converting into “official” currency.

More secure transactions that are encrypted and permanently stored on the blockchain make auditing and reporting easier. Payees will no longer rely on third parties such as banks to handle payments. Banks will also no longer be able to distort transaction values by trading real (government-issued) currencies, skewing their worth – and therefore changing their value.

Smart contracts for the contract or temporary workforce

A smart contract establishes enforceable and immutable rights and responsibilities for all participants. Immutable contracts in HR, for example, may immediately release funds from escrow once employees complete assigned duties, reducing costs to employees and alleviating cash flow problems for businesses.

Compliance and regulations

Employees may simply enforce their “right to be forgotten” rights granted by legislation such as the EU’s General Data Protection Regulation (GDPR) by erasing the encryption key and rendering their personally identifiable information unrecoverable. HR will have greater power to use blockchain in the future to guarantee that workers have control over their data as more strict rules.

Check out how artificial intelligence is changing the recruiting process

Companies using blockchain in HR

One of Japan’s largest employment service firms, Persol Career Co., needed a more efficient way to verify job applicants’ work qualifications. IBM Garage created a private blockchain ledger with IBM Blockchain technology that enables HR managers from a group of businesses and HR organizations to check applicants’ working history and performance while also allowing candidates to submit and share their resumes with select employers or across the network.

AWS Blockchain can also be a good example with its numerous use cases. Amazon Web Services’ AWS Blockchain is a blockchain project for on-demand cloud computing provided by Amazon Web Services to individuals, enterprises, and governments on a metered pay-as-you-go basis. The initiative began in the first quarter of 2019 and currently provides four solutions to its partners and customers. Amazon Web Services offers a ledger database if you need a central ledger that documents all application data changes and maintains an unalterable record of them. This database is fast, permanent, and cryptographically verifiable, so there’s no need to build complicated audit tables or set up blockchain networks. Suppose you want a permanent and verifiable ledger while still allowing multiple users to interact without the need for a trusted central authority. In that case, AWS provides a fully managed, scalable blockchain service.

No matter how useful the services of big companies like IBM or AWS are, blockchain-based HR startups may offer the most suitable solution for you.

HR blockchain startups

When Bitcoin became popular, the term “blockchain” entered common usage – a cryptocurrency that differs from conventional money in that it uses distributed ledger technology rather than fiat currencies. Since then, it has expanded into a variety of applications and is changing HR and workplace management processes all around the world.

Beowulf: Streamlining workplace communication

This 2019 startup uses a decentralized cloud network to simplify internal interactions in various business settings. Among its solutions are private corporate communication, distance learning, remote worker healthcare, and ready-to-use software development kits (SDKs) for specific communication features. The company also has its operating system to change how digital workplaces function.

Beowulf has several well-known clients, including the Vietnam University of Science, Asia Life Insurance Group (AIA), and the U.S.-based cybersecurity firm OPSWAT.

The BeSure Network: Validating workplace safety protocol

BeSure is a 2017 startup that aims to eliminate cases of unverified workers working in hazardous/hazardous environments. Its blockchain technology pulls auditable safety and compliance data from various sources, putting workplace safety on steroids for businesses. Managers (for example, factory floor supervisors), employees, and regulatory bodies can all access data safely.

To keep your business safe, the Sure Network offers smart contracts and automated data entry to make it easier. The system was launched last year and is now in beta testing with backing from multiple safety organizations in the United Kingdom.

Etch: Enabling instant payroll

Etch was founded in 2017 and is a blockchain-based payroll solution that allows individuals to receive pay at their leisure. The money is credited in ECH tokens, which are kept in a digital wallet. Employees can spend money at millions of locations worldwide thanks to the funds an employer deposits, and the Etch card may be used to access cash.

These are exciting times for HR tech start-ups. People process management is being revolutionized by new concepts such as blockchain, AI, and complex analytics. Blockchain, in particular, has now graduated from the incubator and is ready to be applied in real-world situations.

Conclusion

Like other things, implementing blockchain-based solutions to address some of the HR mentioned above issues will take time. Implementing blockchain-based solutions to tackle some of the HR problems will be a slow process.

The first wave, which will likely include blockchain-based candidate verification, is a simple use case. Another option could be real-time employee payments that are less spammy since we can manage our career profile.

The second wave of blockchain-at-work technology might focus on improving talent markets, enhancing the visibility of work, and workers and matches. It could also be about increasing market trust.

The third wave might be about considering the nature of the organization. If we obtain a larger liquid pool that can be utilized for projects, we will have fewer permanent workers with long-term employment contracts. So maybe we’ll see more autonomous organizations and a greater focus on networks of teams.

The good news is that we have the ability to build the next generation of digital workers, and there are certain blockchain characteristics we may include into our digital work platforms that might make things better.

They left in droves. Over 40 million employees quit their jobs in 2021. The mass exodus of workers accelerated last year and shows no signs of slowing down in 2022. While this trend might look bad for the job market, it’s good news for technological development. The Great Resignation is already fast-forwarding mass adaption of AI, both to support current employees and to compensate for the ongoing labor shortage.

Analysts predict a boom in the industrial robotics sector, projected to grow from $16 billion to $37 billion over the next 10 years. Global X’s Robotics, an exchange-traded fund focusing on bots in business, more than doubled its 10-year growth forecast, citing 2022 as “that key inflection point.”

AI-based solutions already respond to calls, provide customer service, operate as cashiers, and assist HR departments in their hiring processes. But future applications of AI technology will include more than just basic tasks. For example, AI could anticipate when an employee is preparing to hand in their two weeks’ notice. Most importantly, it might help establish why they chose to leave.

Thanks to digital footprints — records of online activity, unique as fingerprints — AI-powered HR tools can engage their employees with personalized recommendations. Several startups are doing just that. One such startup is the AI-based solution Giftpack, which set out to transform gifting, now an important part of corporate benefit strategies.

The company’s own AI solution finds gifts from a catalog of over 3 million products available worldwide. By analyzing the gift recipient’s digital footprint, the service can get a sense of their interests and suggest a range of suitable options. For example, if the gift recipient posts Instagram pictures of snowboarding trips and browses winter sports gear sites, they might get some custom accessories.

In the pandemic, companies more than doubled their spending on presents for employees. The average check also increased by almost 70% last year, averaging at around $140 per unit year-over-year.

AI can help large companies eliminate a “one-size-fits-all” approach to their workers, and not just when it comes to gifting. Most businesses don’t know who their employees are, especially if they’re working remotely. What are their interests, hobbies, aspirations? What really motivates them — what makes them get up in the morning?

If HR departments have enough data and, most importantly, AI-based recommendations, they could foster new career opportunities within their organizations. Nestle, Novartis, Unilever, HSBC, and other companies have already implemented an internal talent marketplace in cooperation with Gloat, the future of work startup. The project works with employees to build new skills and try out new roles on a part-time basis.

Mental health and burnout pose another major issue: 46% of people planning to quit their jobs listed work stress as the top reason. AI-based solutions could help HR departments assess employee satisfaction and provide better support to those who need it.

According to HR tech influencer Tyrone Smith, analytic tools promoting active listening and predictive responses can highlight potential trouble, assisting HR staff on a day-to-day basis.

The present moment — with businesses in existential crisis and workers walking out en masse — could be the moment when AI-based HR tools are adopted on a large scale. The work crisis has brought with it a sense of urgency. After The Great Resignation, we might well see the great AI-driven workplace transformation.

This article originally appeared on Hackernoon and is reproduced with permission.

It isn’t a surprise that the amount of data generated in the past few years is astounding—in fact, the data generated within the last two years is close to 90 percent of all data ever generated. However, 80 percent of all enterprise data is unstructured as the numbers continue to grow. Gone are the days when an organization could completely run business intelligence off relational databases. The plethora of information, known as Big Data, gathered over years needs to be mined. Each and every organization stands to benefit by taking this approach, and ultimately drive towards higher profitability. But how you mine data or whom do you hire? Well, you need a data scientist.

Whether it’s retailers who want to track their customer’s purchasing interests and predict the next purchase to run a targeted marketing campaign, or the Department of Homeland Security who would spend hundreds of hours on link analysis to uncover terrorist activity, every organization can benefit by applying data science regardless of their industry.

Who Are Qualified Data Scientists?

Data scientists are individuals who have advanced degrees in mathematics or statistics, extensive domain knowledge, and a strong programming background. According to the Burtch Works study, 46 percent of data scientists have PhDs, while 42 percent hold Masters Degrees. However realistically, these candidates are not an easy find.

McKinsey Global Institute published a report stating that by 2018, the U.S. alone may face a 50 to 60 percent gap between supply and requisite demand of deep analytic talent. The institute further projected that in U.S. alone, four million positions will seek individuals with strong quantitative and analytics background by 2018. Unfortunately, there exists a gap to find a data scientists in almost every industry, whether it is finance, aerospace, hi-tech, or any other.

As Demand for Data Scientists have Increased, So Have Salaries

According to Indeed.com, data-scientist salaries are 113 percent higher than the average salary for all jobs in United States. In fact, the average data scientist salary, of 123,000 USD, increased by roughly 20 percent in the past two years. The 2014 Burtch Works study of salaries and data scientists concluded that data scientists with nine or more years of experience draw on average a salary of 150,000 USD, which escalates at the managerial level. More specifically, an individual managing a team of three or less earns an average of 140,000 USD while someone who manages a team of ten or more would earn an average of 232,500 USD. When compared to the average salary for a doctor (183, 940 USD) or a lawyer (131,990 USD) data scientists are very well compensated based on data published by the U.S. Bureau of Labor Statistics. Although it takes an advanced degree, strong business acumen and intellect to land at a high-paying job, organizations will pay top dollar for data scientists who providing value to the business.

Decision Time: Hiring Within and Growing or Hiring External Candidates?

In 2014, Accenture surveyed its clients on their Big Data strategies and found that more than 90 percent of the respondents planned to hire employees with expertise in data science. Training employees to be data scientist are difficult but not entirely impossible since they already have domain knowledge and an understanding of company’s business. However, to truly fill shoes of a data scientist, an employee needs to be a great researcher with background in statistics, a great problem solver, and a great software developer. Unfortunately, many organizations believe that employees proficient in one area can be good data scientists.

In addition to teaching an employee how to use Big Data technology and tools, the employee needs to have an aptitude and an interest in data science. Without this passion, employees in the data scientist roles will not deliver the compelling insights an organization seeks to gain. Employees within a company tend to get bored doing the same task over and over, and search for different roles and responsibilities within the company or elsewhere. When this is the case, it diminishes the value and effort it takes to train an employee to be data scientists.

Hiring from the outside is more straight-forward. Organizations can conduct extensive interviews which include, asking for sample work or academic presentations and determining the candidate’s aptitude for problem solving to better understand his or her data exploring capabilities. Another beneficial characteristic is being a team player. Employees can have great skillsets, but an organization can build great things with a team.

It is imperative to setup an interview as a two-way street; the candidate should feel that the company they consider joining has an interesting problem to solve. They should be curious not only about how the company is solving critical business issues using data science but also how they can add value. The ideal individual can make solid contributions to grow a team that can take the company further and create long-lasting value in an organization’s success.

Ensuring Long-Term Success of Data-Science Driven Organizations

Data scientists who succeed become role models for what’s valued in an organization, and that defines company’s culture. The best way to ensure long-term success is to clearly articulate and provide transparency about key company and team values. Over time, an organization’s HR practices, including hiring decisions, tenure of employees, and company success will help define and reflect the key values and culture. A company’s culture is its core asset, and chaos arises when key values start to fall apart.

Muddu Sudhakar, CEO of Caspida, is an experienced entrepreneur in the world of data driven technology. He previously served as VP & GM at VMware and Pivotal from 2010 to 2014 for Big Data Analytics and Cloud Services. At VMware and Pivotal he held responsibility for range of projects, including Software-defined Storage and Mobile Security. Muddu was also Co-Founder of Cetas, which was acquired by VMware in 2012.

(image credit: COD Newsroom)

Human capital management software vendor, Workday, has developed a new prediction technology that flags moments where employees might quit providing employers a heads up to resolve issues.

VMware, the US based cloud and virtualization software and services provider is testing Workday’s latest offering, reveals Bloomberg.

“We’ve had some great results to date with the data,” noted Amy Gannaway, VMware’s senior director for worldwide human resources information systems, while attending a Workday conference in September last year. She explained that the application provided “a very high percentage” of accurate predictions for which employees would leave the company.

The technology uses machine learning to glean patterns employee activity, “when promotions were last handed out, regional factors, changes in the industry and other data to make its predictions.” What’s more is that the system improves over time as employers train the system, Bloomberg reports.

Read more here.

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(Image credit: Benjamin Watson)