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How to Become a Freelance Artificial Intelligence Engineer

4 minute read | October 29, 2021
Sakshi Gupta

Written by:
Sakshi Gupta

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Artificial intelligence aims at imparting human cognitive abilities to machines and business processes. Just imagine how smart machines would get with artificial intelligence giving them the skills of reasoning and human-like thoughts. Artificial intelligence has always looked at building systems with human intelligence for better task execution and efficiency. Over the last few years, there has been significant progress in the fields of machine learning and deep learning, which has led to an enhanced ecosystem for AI applications in business processes.

Artificial Intelligence today is finding application in almost all domains like healthcare, supply chain and logistics, transport, banking and agriculture, to name a few. This comprehensive study from McKinsey depicts AI as the next frontier of a technological revolution in business. Although artificial intelligence is often portrayed as a job killer for the current workforce, the reality is somewhat different. Experts predict that AI will create more and better opportunities for people who are willing to up-skill themselves. Businesses will strive towards inclusive technology giving shape to hybrid workforces. In this blog, let’s find out how to become a freelance artificial intelligence engineer.

How to Become a Freelance Artificial Intelligence Engineer

  1. Coding skills

  2. Knowledge of machine learning

  3. Data analytics

  4. Liaison between different teams

There are numerous freelancing platforms where you can kick start your career as a freelance artificial intelligence engineer. But you can only be successful as one once you have the required skillset and can market your skills effectively. Let’s take a look at the prerequisites you must have.

  • Coding skills

For considering a career as an artificial intelligence engineer, you must be proficient in coding languages like Python (most popular language in the field of data science), C, C++. Since AI is multi-disciplinary technology, knowledge of machine learning frameworks like TensorFlow and expertise in data analytics languages like R is also needed to be successful.

  • Knowledge of machine learning

Machine learning is an important sub-domain of AI as it gives the systems the ability to learn dynamically and have a better response to scenarios. Artificial intelligence engineers must be experts in developing machine learning algorithms to tackle real-world problems.

  • Data analytics

Artificial Intelligence Engineers must have the ability to utilise data analytics tools to effectively power AI-driven applications. Data plays an important role in today’s internet age and is of no use if not handled appropriately.

  • Liaison between different teams

Although in the freelancing scenario you must always strive towards working as a full stack developer, at times you might want to act as a bridge between different teams like Data Scientists, ML Engineers or senior AI Engineers depending on the scale of the project. Artificial intelligence Engineers must have a bird’s eye view of proceedings in such scenarios.

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Why are Artificial Intelligence Engineers Critical for Business Success?

Last few years have seen a major paradigm shift in AI implementation in businesses. AI-driven organisations are doing better as compared to their peers as AI is driving automation and analytics with better efficiency. Since the application of AI is not limited to a domain, a good AI workforce is the need of the hour for organisations.

That said, in their early stages, AI-driven applications will need considerable support from a professional workforce since the success of a business will depend on the effective full-scale deployment of AI applications.

How Freelancing Helps Businesses and Individuals Achieve More

Pursuing AI as a career is an exciting thought for many nowadays, but how to get started is the first obstacle they face. A smart way to tackle this conundrum is to consider starting out as a Freelance Artificial Intelligence Engineer. Freelancing is flexible enough to allow you to experiment in relatively newer technologies like AI without taking the risk of actually quitting your full-time job. It provides you with an option to keep learning, and at the same, be on the lookout for industrial projects. You may think so, but freelancing is not just limited to part-time opportunities as many professionals prefer full-time freelance work culture over conventional jobs. It offers several benefits like

  • Flexible working hours
  • Better work-life balance
  • High pay for both full time and part-time projects
  • Flexibility to work as much you want

Freelance talent has become an instant hit among businesses too. They no longer have to expand their workforce for just one project, since with freelancers they can onboard them on a project basis. Some businesses have even adopted the concept of the complete remote workforce as that saves them the extra cost of setting up an office and building infrastructure. Here’s a study from Upwork giving insights on how fortune 500 companies like Dropbox, Airbnb and Samsung are on a lookout for freelance talent saving crucial time and money.

Organisations have realised the potential of AI-driven businesses and even in early stages have started working on boosting AI infrastructure and talent pool with extensive investment. Considering this, AI is set to create jobs at a significant rate, predicts KPMG’s report on ‘AI transforming the Enterprise’. The freelance talent will play a major role in occupying these jobs as most of the early adopters of AI think outside the box and have no issues considering freelance talent. Although freelancing is a demanding job at the early stages of developing a profile, you will reap benefits later if you focus on creating long term relationships with clients. Machine learning and data analytics freelancers are the highest paid ones among freelance skills, and thus the number is only bound to grow in future.

Wondering where to begin your Freelance Artificial Intelligence Engineer career? Try taking an online course to acquire the necessary skills. We recommend opting for a focused AI and ML learning program offered by Springboard that offers real-world projects, 1:1 mentorship, career coaching and a job guarantee. Having launched thousands of students across 100+ countries into professional success, Springboard is an avenue that can help you take your career to new heights.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.