Opportunities in Artificial Intelligence: 15 Things You Didn’t Know About
Robotic process automation has scared a lot of professionals lately due to its tendency to replace many jobs done by humans. As a result, many tech-savvy people tend to avoid AI career skills thinking that they would one day be taken over by machines. Well, the fact is machines do a lot of jobs – at speeds, accuracies, and consistencies that humans cannot. But machines still have their limitations. And it is this limitation that the human mind can leverage in the job market. In this article, we discuss 15 AI career opportunities you probably aren’t aware of, and something machines aren’t likely to do any time soon.
1. Machine Learning Engineer
Machine learning engineers are highly sought after. They earn an estimated median salary of $114,856 yearly. Machine learning engineers are typically responsible for designing and managing platforms for machine learning projects.
Machine learning engineers play a role that is at the heart of every artificial intelligence project. It is best executed by those who have a background in applied research and data science. Additionally, there is the need for a prospective machine learning engineer to have a thorough understanding of multiple programming languages and be an AI programmer. A machine-learning engineer should also be able to work with predictive models and manipulate natural language processing in working with massive datasets
Companies typically look for candidates who are highly experienced agile development practices and have a good understanding of leading software development ide tools like eclipse and IntelliJ.
Studying many job listing sites reveal that companies prefer hiring individuals who possess a master’s or doctoral degree in mathematics or computer science. To increase your prospects of getting hired as a machine learning engineer, you should demonstrate an in-depth knowledge of programming languages like java, Python, and Scala. It will be an added advantage to be a technology professional with good mathematical skills. Most jobs also prefer experts in machine learning, deep learning, and experience with cloud applications.
2. Data Scientist
As a data scientist you are responsible for collecting, analyzing, and interpreting large, complex data sets by the use of predictive analytics and machine learning. Data scientist also plays a leading role in developing algorithms to be use in collecting and cleaning of data for analysis. The average annual earning of data scientist is $120,931. To be successful in the type of data science a candidate should have good command of big data platforms and tools like, hive, Hadoop, MapReduce, pig, and spark. The data scientist should also have good experience with statistical computing languages and programming languages like Perl, Python, Scala, and SQL.
Hiring companies mostly prefer data scientist who are highly educated with the master’s or doctoral degree in computer science. But sometimes and advanced degree in electrical engineering or mathematics is enough, except for data scientist who want to play the role of an AI developer, in which case an advanced degree in computer science becomes a requirement. To boost your chances of being hired you should possess at least three years of experience working with machine learning. A good knowledge and experience using cloud tools like amazon s3 and Hadoop platform will be an advantage. A data scientist to mental health good analytical and communication skills in order to communicate their findings properly business leaders.
3. Business Intelligence Developer
Business intelligence developer is one of the top carrier in artificial intelligence. The primary role of a business intelligence developer is to analyze complex data sets for business and market trends. This whole game froze the efficiency and profitability of the business and that makes it one of the case in high demand today with an annual median salary of $92,278.
as a business intelligence developer, you will be responsible for designing modelling and maintaining complex data in highly accessible data platforms on the cloud.
To do this job well you need to have strong technical and analytical skills. You should also be able to communicate with non-tech savvy college and demonstrate strong problem-solving skills. A business intelligence developer is typically required to have a bachelor’s degree in engineering computer science or related field however combining one or more of these qualifications with on-the-job experience will be an added advantage. It follows that a candidate should have considerable experience in data warehouse design, data mining, SQL queries, SQL server integration services, SQL server reporting services, and bi technologies. Artificial intelligence continues to grow across many industries demand for business intelligence developers when increase.
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4. Research Scientist
Research scientist is one of the leading careers in artificial intelligence. The research scientist is typically an expert in multiple artificial intelligent disobedience like applied mathematics, machine learning, deep learning, and computational statistics. For a candidate to stand a good chance of being hired they must demonstrate the proper knowledge and experience in computer perception, graphical models, reinforcement learning, and natural language processing. The average annual salary of research scientist is about a hundred thousand dollars.
Just like to the case of data scientist, a research scientist is required to have advanced masters of doctoral degree in computer science. But there are companies that accept an advanced degree and related technical fields backed up by experience. Most companies prefer technology professionals with sound of a standard of benchmarking, parallel computing, distributed computing, machine learning, and artificial intelligence.
5. Big Data Engineer/Architect
Big data engineers and architects are among the highest-paid experts in the field of AI. They make an average of $151,000 annually. These engineers play a vital role in developing an expert system that allows business systems to communicate with each other and collate data. Most employers prefer experts that have a PhD in mathematics computer science or a related field. Big data engineers and architects define by data scientists in that their work is more involved since they are tasked with planning, designing, and developing the big data environment on Hadoop and spark systems.
Candidates of big data engineering and architecture have to demonstrate significant programming experience with C++, java, Python, and Scala. They are also expected to have in-depth knowledge and experience of data mining, data visualization, and data migration.
6. Computer Vision Engineer
Computer vision refers to the ability of computers to not only see images but also make some sort of sense out of those images, like detecting movements and determining distances. The technology has applications in medicine, defense, manufacturing, and many more.
The work of a computer vision engineer has to do it applying computer vision research that is based on a large amount of data to solve practical problems. Do you say engineers spend a lot of time working on research and implementation of machine learning primitives and computer vision for companies? The collaborate with other artificial intelligence experts to facilitate the implementation of novel embedded architectures. To succeed as a computer vision engineer why should have considerable experience with a number of systems such as image recognition machine learning and segmentation.
To pursue a career in computer vision engineer you will first need to obtain a bachelor’s degree in computer science, information system, or other similar disciplines. Then you will obtain the master’s degree in computer science or computer engineering preferably specializing in a relevant course.
Computer vision engineer you need to have a good knowledge of working with linear algebra map libraries and related computer vision libraries. We also need software skills in the areas of database management, development environment, and component of object-oriented software. It is also important to have analytical and critical thinking skills because you’ll be dealing with complex problems that require the analysis of results to derive accurate conclusions. Equally important is the ability to think logically, reason clearly, and be detail oriented.
7. Algorithm Developer
The major work of an algorithm developer typically involves researching, writing and performance testing algorithms. They are responsible for working to implement algorithms and then analyze and modify them as required. Algorithms generally use delta from a system to execute actions processes or reports. So that means each algorithm you built must first take into account the goals and then work to achieve specific results. It is common to see algorithm developers collaborate with a team in creating theories and then performing research and test until the perfect and efficient algorithm.
to succeed this kind of job you need understanding of r and C++ and Python optionally. If the employer requires that you have knowledge and experience in neutral nets, then at least a bachelor’s degree in mathematics, computer science or related fields is a necessity.
8. Junior Data Scientist
Data science is one of the best fields of artificial intelligence to be in right now. This is because of its growing demand high pay. Data science is the third best job in America according to Glassdoor. It has an average salary of $107,801 with an overall job satisfaction of ⅘. Because the amount of existing data is expected to increase over time, the need for data scientists will continue to grow.
A big advantage of data science is that the field is very broad and offers flexibility. Data scientist normally develops symptoms to sort through data and collect helpful information therein. A major part of the job is to interpret data into something easier to understand and use for their employers. The two men have that data scientist up with on a regular basis is to find specific data sets and development relevant scientific questions. Data scientists differ from data analyst in that data scientist use the concepts behind the scientific methods to analyze, interpret, and solve business problems.
there are specific skills that an aspiring data scientist should learn. This includes statistics, programming, and communication. Other less necessary skills that can be an added advantage include:
- level of education (MS & PhD are highly preferred)
- professional development (workshops, MOOCs, certifications, etc.)
- project portfolio (proof of analytics applications)
- work experience (proof of the 3 mandatory skills in the industry setting)
- results (proof of finishing a project in your project portfolio or work experience)
Data scientist and junior data scientist do similar work, there is a great difference between them regarding the mandatory core skills required of a data scientist. A junior data scientist may only need about 2 or 3 of the mandatory skills that a data scientist must possess. For example, junior data scientist may not have strong programming or communication skills but will still be qualified for the position since they are more likely to be dedicated for a single project or purpose
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9. Developer Consultant
In the world of software development, a consultant’s job is to help others be their best. The consultants work with an individual or team to help detect problems, identify strengths and areas of improvement. On the other hand, a developer consultant is responsible for a number of things that may involve coding as well. For example, a developer consultant may write code, manage projects, create stories, facilitate retrospectives, improve business processes, host iteration panning meetings, convey the values of paying down technical debt, play QA/tester roles, etc. His main mission is to elevate the abilities of the team.
To work as a developer consultant, you need to work for a software consultant company that hires you out from time to time or regularly. For example, you could be working for oracle and some large company may need assistance to set up middleware. Your permanent employer (in this case oracle) hires you out on a contract basis to assist the company.
To become a developer consultant, you can establish your own consulting firm and let recruitment agents know that you are available for hire. The recruitment agents may hire you out from time to time subject to certain contractual terms. You can just do it all alone, but that would be much more difficult.
10. Director of Data Science
As a director of data science, you will lead an entire data team of scientist and engineers. You will manage the junior data science teams to make sure that projects are executed accordingly. You will also lead the departments engagement with business stakeholders and executive partners where these dignitaries work to enhance the existing data management methodologies and develop new approaches and methods.
As a director of data science, you will lead both leadership and supervisory roles in data departments. You will develop a team’s culture, hiring standards, and HR policies. You will also develop the data science vision and oversee its adoption throughout the company’s departments. The director of data science also oversees the data science department’s training and competency development, determine practices and work standards. You will enact projects that improve performance of departments while focusing on revenue growth and achievement of the company’s overall targets and objectives.
Additionally, you will also lead the department in developing new insights, advanced modeling techniques, and data science capabilities. In his leadership capacity, you will also be responsible for the preparation of white papers, conference presentations, and scientific publications.
11. Lead Data Scientist
As a lead data scientist, you’ll be managing the data science team during the planning and building of analytics models. It is necessary to have a strong knack for solving problems and for statistical analysis. Your major aim will always be to improve products and business decision by deriving as much insight as necessary from data. You can expect to be tasked with one or more of the following:
- Supervise a team of data scientists, machine learning engineers and big data experts
- Lead data mining and collection procedures
- Ensure data quality and integrity
- Interpret and analyze data problems
- Conceive, plan and prioritize data projects
- Build analytic systems and predictive models
- Test performance of data-driven products
- Visualize data and create reports
- Experiment with new models and techniques
- Align data projects with organizational goals
You will be required by employers to have a combination or all of these skill sets:
- Proven experience as a data scientist or similar role
- Solid understanding of machine learning
- Knowledge of data management and visualization techniques
- A knack for statistical analysis and predictive modeling
- Good knowledge of r, Python and Matlab
- Experience with SQL and non-SQL databases
- Strong organizational and leadership skills
- Excellent communication skills
- A business mindset
- A Degree in computer science, data science, mathematics or similar field is a plus
12. User Experience
The job of a UX involves working with products, even those that incorporate AI, to ensure that consumers understand how to use them. This role traditionally existed outside of the AI sector, but as the use of AI technologies continue to grow, there is increasing demand for UX specialists that are trained for UX jobs within an AI framework.
As a user experience specialist, you are in charge of understanding how humans use equipment. What you discover will help computer scientists apply modifications that lead to the production of more advanced software. In the context of AI, the roles of a UX engineers may include understanding how humans are interacting with these tools in order to develop functionality that better serve them.
Among the most prominent examples of how user experience involves technology is the case of apple Inc. The mac OS was developed in order to solve the user experience of complexity regarding windows OS. To use the windows OS effectively, a user requires more advanced technical understanding than if they use mac. In the same manner, apple developed the iPhone. iPhone focuses on improving user experience by understanding how people interact with their phones, including what is intuitive and what is not. With this understand, iPhone designed the best phone they could to meet this need.
13. Natural Language Processing
A great number of the most popular consumer applications of AI today revolve around language. These include chatbots, virtual assistants, predictive texting on smartphones, etc. AI tools have been used to replicate human speech in a wide range of formats. To do this effectively, developers leverage the knowledge of natural language processers. These developers are individuals who have both the language and technology skills needed to assist in the creation and perfection of these tools. Natural language processing is a big AI field that appear poised to continue to grow.
As an aspiring natural language processing expert, you should know that there are many applications of natural language processing, and the responsibilities of the experts in this field will vary. But generally, the experts in these roles will deploy their complex understanding of both language and technology to develop user-friendly interfaces that enables computers to successfully communicate with humans.
This critical field is facing a real shortage of experts. A lot of products are trying to interact with machines through language, but language is really hard. Experts of natural language processing should expect to earn salaries well beyond the average earned by their peers for the foreseeable future. The average annual salary of these experts can go as high as $107,000.
14. Computer Science & Artificial Intelligence Research
Most AI careers aim to explore the application or function of AI technology. But computer science & AI research is more about discovering knowledge to expand the field of AI itself. Someone will always develop a faster machine – that is how it has been from day one. There will always be people pushing the edge of AI, and those are called computer scientists. Their major role is conduct research related to AI.
The responsibilities of these scientists vary widely depending on their specialization or particular role in the research field. Some may focus on expanding the data systems used in AI projects. Others might manage the development of new software to uncover new limits in the field. There are those who will be responsible for overseeing the ethics and accountability that comes with the creation of AI tools. The roles of all individuals in this filed will streamline towards unraveling the possibilities of AI technologies and then help implement changes in existing tools to reach that potential.
Since these AI research experts are at the top of advancement in the field, their job outlook is quite positive. Estimates by the New York times hold that high-level AI researches at top companies make a lot more than $1,000,000 a year as of 2018. Lower level employees make between $300,000 and $500,000 per year in both salary and stock. While those in base-level AI research can expect to earn an average salary of $83,490 annually.
15. Azure Data Scientist
Microsoft azure has a large-scale adoption around the world, and that is what makes it so popular. The demand for careers in azure data scientist roles is continually increasing. Every modern business needs data and the demand for data-driven professionals like azure data scientists would continue to pay highly for professionals around the world.
As an azure data scientist, your job is majorly to help translate customer requirements into POCs for positive solutions. The job pays an annual average of $85,000 – $160,000.
To become an azure data scientist that finds favor with employers, you need to have an MS in computer science or similar field. Then you should master the following skills:
- R and Python programming
- 2+ years in data science
- Have working experience in platforms such as AWS and Azure services
- Machine learning, deep learning, and NLP
As AI is a continually expanding and vast field, we haven’t captured all the promising career opportunities in this article. New ones will continue to emerge as some phase-out. As a prospective AI expert, it is important that you keep tabs on industry changes in sectors of interest to you because these changes have a direct effect on the type of skill that will be needed in the nearest future. You may also keep tabs on what is happening in the AI research world.