Career Opportunity in Machine Learning at 2021

Career Opportunity in Machine Learning at 2021

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In 2021, the focus on digitalization is as solid as ever before. Machine learning and AI help IT leaders and global enterprises to come out of the global epidemic with minimal loss. And the demand for specialists that know how to apply data science and ML techniques continues to grow. In this post, you will find some career options that absolutely will be in demand for decades to come. And there is a twist ― AI has stopped being a completely technical field. It is tangled with law, philosophy, and social science, so we’ve included some professions from the humanities field as well.

Popular ML jobs to choose in 2021 Programmers and software engineers are some of the most desirable specialists of the last decade. AI and machine knowledge are no exception. We have conducted research to find out which occupations are the most popular and what skills you need for each of them.

  1. Machine learning software engineer A machine learning software engineer is a computer programmer who is working in the field of artificial intelligence. Their task is to create algorithms that enable the machine to analyze input information and understand causal relationships among events. ML engineers also work on the development of such algorithms. To become an ML software engineer, you are necessary to have excellent logic, analytical thinking, and programming skills. Employers regularly expect ML software engineers to have a bachelor’s degree in computer science, engineering, mathematics, or a related field and at least 2 years of hands-on experience with the carrying out of ML algorithms (can be obtained while learning). You need to be capable to write code in one or more programming languages. You are expected to be used to with relevant tools such as Flink, Spark, Sqoop, Flume, Kafka, or others.

  2. Data scientist Data scientists put on machine learning algorithms and data analytics to work with big data. Quite often, they effort with unstructured arrays of data that have to be cleaned and preprocessed. One of the main tasks of data scientists is to discover designs in the data sets that can be used for predictive business intelligence. In order to successfully work as a data scientist, you need a strong mathematical background and the ability to essence on uncovering every small detail. Bachelor’s degree in math, physics, statistics, or processes research is often required to work as a data scientist. You need to have robust Python and SQL skills and outstanding analytical skills. Data scientists often have to current their findings, so it is a plus if you have experience with data visualization tools (Google Charts, Tableau, Grafana, Chartist. js, FusionCharts) and outstanding communication and PowerPoint skills.

  3. AIOps engineer AIOps (Artificial Intelligence for IT Operations) engineers help to develop and deploy machine learning algorithms that analyze IT data and boost the efficiency of IT processes. Middle and large-sized businesses dedicate a lot of human capitals for real-time performance watching and anomaly detection. AI software engineering allows you to mechanize this process and optimize labor costs. AIOps engineer is essentially an operations role. Therefore, to be hired as an AIOps engineer, you need to have information about areas like networking, cloud technologies, and security (and certifications are useful). Understanding with using scripts for automation (Python, Go, shell scripts, etc) is quite necessary as well.

  4. Cybersecurity analyst A cybersecurity analyst identifies data security threats and risks of data leakages. They also tool measures to protect companies against information loss and ensure the safety and privacy of big data. It is significant to protect this data from malicious use because AI systems are now ubiquitous. Cybersecurity experts often need to have a bachelor’s degree in a technical field and are expected to have overall knowledge of security frameworks and areas like networking, operating systems, and software applications. Certifications like CEH, CASP+, GCED, or similar and knowledge in security-oriented competitions like CTFs and others are looked at favorably as well.

  5. Cloud architect for ML The majority of ML companies today prefer to save and procedure their data in the cloud because clouds are more reliable and scalable, this is especially important in machine learning, where machines have to deal with very large amounts of data. Cloud architects are responsible for management the cloud architecture in an organization. This profession is especially relevant as cloud technologies become more multifaceted. Cloud computing architecture includes everything related to it, including ML software platforms, servers, storage, and networks. Among useful expertise for cloud architects are experience with architecting solutions in AWS and Azure and expertise with configuration management tools like Chef/Puppet/Ansible. You will need to be talented to code in a language like Go and Python. Headhunters are also looking for expertise with watching tools like AppDynamics, Solarwinds, NewRelic, etc.

  6. Computational linguist Computational linguists take part in the making of ML algorithms and programs used for developing online dictionaries, translating systems, virtual assistants, and robots. Computational multilingual person have a lot in common with machine learning engineers but they combine deep knowledge of linguistics with an understanding of how computer systems method natural language processing. Computational linguists regularly need to be able to write code in Python or other languages. They are also regularly required to show previous experience in the field of NLP, and companies expect them to provide valuable suggestions about new innovative approaches to NLP and product development.

  7. Human-centered AI systems designer/researcher Human-centered artificial intelligence systems designers make sure that smart software is created with the end-user in mind. Human-centered AI must learn to collaborate with humans and unceasingly increase thanks to deep learning algorithms. This communication must be seamless and suitable for humans. A human-centered AI designer must possess not only practical knowledge but also understand cognitive science, computer science, psychology of communications, and UX/UI design. Human-centered AI system designer is often a research-heavy position so applicants need to have or be in the process of obtaining a PhD degree in human-computer interaction, human-robot interaction, or a related field. They must make available a portfolio that features examples of research done in the field. They are often predictable to have 1+ years of experience in AI or related fields.

  8. Robotics engineer A robotics engineer is someone that projects and builds robots and complex robotic systems. Robotics engineers must think about the mechanics of the future human assistant, envision how to collect its electric parts, and write software. Thus, to become an expert in this field, you need to be well-versed in mechanics and electronics. Since robots regularly use artificial intelligence for things like dynamic interaction and obstacle avoidance, you will have plenty of opportunities to work with ML systems. Employers usually require you to have a bachelor’s degree or greater in fields like computer science, engineering, robotics, and have experience with software development in programming language like C++ or Python. You as well need to be familiar with hardware interfaces, including cameras, LiDAR, embedded controllers, and more.

Bonus: AI career is not only for techies AI jobs for non-tech professionals If you don’t have a technical background or want to change to a completely new field, you can check out these emerging professions.

  1. Data lawyer Data lawyers are experts that guarantee security and compliance with GDPR requirements to avoid millions of dollars in fines. They know how to properly look after data and also how to buy and sell this data in a way that avoids any legal complications. They also know how to manage risks arising from the processing and storage of data. Data lawyer is the professional of the future; they stand at the connection of technology, ethics, and law.

  2. AI ethicist An AI ethicist is someone who behaviors ethical audits of AI systems of companies and proposes a comprehensive plan for improving non-technical aspects of AI. Their goal is to remove reputational, financial, and legal risks that AI adoption might pose to the organization. They also make sure that companies tolerate responsibility for their intelligent software.

  3. Conversation designer A conversation designer is big shot who designs the user experience of a virtual assistant. This person is an efficient UX/UI copywriter and specialist in communication because it is up to them to translate the brand’s business necessities into a dialogue.

*How much does an ML specialist make? *

According to Indeed.com, salaries of ML specialists vary dependent on their geographical location, role, and years of experience. However, on regular an ML specialist in the USA makes around $150,00 per year. Top companies like eBay, Wish, Twitter, and AirBnB are prepared to pay their developers from $200,000 to $335,000 per year. At the time of writing, the highest paying cities in the USA are San Francisco with an normal of $199,465 per year, Cupertino with $190,731, Austin with $171,757, and New York with $167,449.

*Industries that require ML/AI experts *

Today machine learning is used almost in every one industry. However, there are businesses that post more ML jobs than others:

*Transportation. *

Self-driving vehicles starting from drones and ending up with fully autonomous vehicles rely very deeply on ML. Gartner expects that by 2025, autonomous vehicles will surround us everywhere and achieve transportation operations with higher accuracy and efficiency than humans.

Healthcare.

In diagnostics and drug finding, machine learning systems allow to process huge amounts of data and detect patterns that would have been missed otherwise.

*Finance. *

ML allows banks to improve the security of their operations. When something goes wrong, AI-powered schemes are able to identify anomalies in real-time and alert staff about potentially fraudulent transactions.

*Manufacturing. *

In factories, AI-based machines help to automate quality control, packing, and other processes, while agreeing human employees to engage in more meaningful work.

Marketing.

Targeted marketing campaigns that involve a lot of customization to the needs of a specific client are reported to be much more effective across different spheres. Conclusion Have you found anything that interests you? Let us know if there are any more occupations of the future that we must mention. We hope that no substance if you choose a machine learning career from this list or not, you will find useful materials to continue learning about ML in our blog. Here are some that might interest you: Top Resources to Learn Machine Learning How to Participate in a Kaggle Competition Where to Find the Best ML Datasets If you wish to read an article on a sure topic, feel free to suggest it on Twitter, and we will try to cover it in the future!

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