Careers in machine learning: Jobs that are forging the future

The AI takeover is finally underway but not like the nightmare we (or The Matrix) had imagined it to be. It is gradually becoming a part of various industries such as healthcare, education, and e-commerce and creating jobs on its way so that AI and humans can work together to create a more efficient world. However, the global workforce will have to be better prepared for this transition by reskilling and upskilling themselves. Since machine learning is a crucial part of AI, many ML jobs have cropped up in recent years. Read on to find out if anything sparks your interest and how you can prepare for it.

What are the different kinds of ML jobs?

A quick search on Google for machine learning jobs paints an optimistic picture. If you have been thinking about starting out, then here are some ML career options that you can explore:

1. Machine learning engineer

Machine learning engineers build products that use ML techniques. This takes place in multiple steps including data collection and preparation, building a ML model, writing production level code, and taking care of any issue that may arise. This is called a machine learning pipeline and ML engineers take complete charge of executing it. They can also be responsible for improving existing ML models. 

Other day-to-day activities consist of staying abreast with recent developments in the field of machine learning and brainstorming ideas, interacting with different teams such as data science, product management, and operations, and publishing blogs and research.

The role of a ML engineer also varies depending on the company they work with. Those working at start-ups tackle various responsibilities such as creating data pipelines, data preparation, and model deployment. On the other hand, in bigger companies, they tend to have a specific role such as managing ML infrastructure.

Salary: Entry-level ML engineers can earn an average gross salary of Rs. 501K per annum while mid-career professionals can expect an average income of 12 lakhs, according to PayScale.

Eligibility and skills:

1. Bachelor’s/Master’s/PhD in computer science and related fields or equivalent work experience. While computer science may seem like the ideal degree to break into this field, many professionals from physics, psychology, and economics have also switched to machine learning.

2. Sound knowledge of mathematics and statistics.

3. Excellent programming skills in one or more machine learning languages such as Python, R, and JavaScript because ML engineers are directly engaged with developing software. Python is the most in-demand.

4. Familiarity with using different ML tools such as TensorFlow, Scikit-learn, PyTorch, Keras, and Pandas.

5. Knowledge of analytics tools such as Hadoop, Spark, and Hive.

Tips from ML engineers:

1. Before applying, take time to enhance both your technical knowledge and academic knowledge of machine learning. You can exhibit your technical knowledge through projects, GitHub profile, and a website. Additionally, you should have a blog, publish research papers, or anything that shows that you are passionate about the impact ML is creating.

2. Work on projects that set you apart from other candidates. This could include working on a project in a field that you are interested in. This will also help you in engaging with real-world problems that can profit from machine learning.

3. While choosing an algorithm, think about why you are choosing to go with one and not the other. Hiring managers often dig deeper into the thought process of candidates. They like to know why a candidate chose a certain project or algorithm.

4. Try to specialize in a particular field of machine learning rather than being a jack of all trades. Because machine learning is an ever-evolving field, there will always be something that you don’t know.

2. Data scientist

Who knew that learning machine learning can allow one to call themselves a scientist? Let’s learn about the work one has to put in to earn this title.

At a basic level, data scientists translate a business problem into a computer problem and then come up with a solution for it. Once they articulate the problem, they figure out the logic to solve it, which is called an algorithm. Next, they find the data to train this algorithm. This data must be cleaned so that the algorithm gives good results. Once the algorithm is trained, they test it and, then if all is in order, they finalise the prototype. This prototype is passed onto machine learning engineers to implement for end-users.

Data scientists must be good at conveying business insights that can be put into practise. This includes gaining insights that can give the client a competitive edge in the market, improving a product, creating a new product, helping discover untapped/new opportunities, etc.

As you can infer, data scientists operate both in the world of machine learning and analytics. Their work can include working with databases, collecting data and structuring it, data wrangling, building ML models, etc.

You may have noticed the overlap between the role of a data scientist and machine learning engineer. This shared ground has allowed many professionals to transition between these roles. According to PayScale, this is one of the most common career paths for machine learning engineers.

This is usual for a lot of machine learning jobs where the skills are transferable to venture into a new job.

Salary: Newbies can expect an average gross salary of Rs. 511K per annum whereas, professionals who have been at it for a while can expect to earn 13 lakhs.

Eligibility and skills:

1. A graduate degree usually in any specialization. You don’t necessarily need a computer science degree to become a data scientist as long as you have put in the work to qualify for the role such as taking online courses, participating in competitions, building projects, and publishing papers. However, the eligibility criteria can differ across companies.

2. Proficiency in programming languages such as Python, R, SQL, Java, and Scala.

3. Adept in statistics and mathematics.

4. Familiarity with data structures and commonly used algorithms.

5. Strong analytical skills to identify patterns in data and derive business insights from it. 

6. Communication skills so that you can convey the insights in an understandable language and visuals! You get additional brownie points if you have brilliant storytelling skills.

3. NLP engineer

NLP stands for Natural Language Processing, which is a field that combines computer science and linguistics. 

Although we have developed languages to interact with machines, scientists have been striving for machines to understand human languages in its natural form. NLP is the field that’s concerned with developing technology to make this a reality. What makes this enterprise a huge task are the nuances of each human language. For example, there are words that can be used as both nouns and verbs.

Sentence 1: I love chocolate cakes.

Sentence 2: Chocolate cake was my first love.

It is the job of an NLP engineer to come up with ways in which machines can understand these differences. One of the ways is creating machine learning models. These models allow machines to understand text/speech by analysing data. For example, Google Translate works by analysing billions of translations in documents, books, and websites. By analysing, it discovers the rules of a language that it uses for translations.

Salary: An NLP engineer has an average base salary of Rs. 612K per year, according to Glassdoor.

Eligibility and skills:

1. Bachelor’s/Master’s/PhD in computational linguistics or computer science. This will again vary across companies and, you may not require any specialization at all!

2. Strong understanding of core NLP concepts.

3. Skilled in using Python or other commonly used programming languages such as Java, C, or R.

4. Ability to use ML algorithms.

5. Should be able to use databases such as MySQL and Redshift.

6. Experience with statistical software such as R.

4. Computer vision engineer

Have you ever been tagged in an unpleasant Facebook photo of yourself with a friend? You can blame computer vision for that! Computer vision identifies your face in a picture and allows others to tag you.

While humans can see an object and analyse it, computers don’t have this innate ability and are programmed to do so. This is often done through machine learning algorithms by computer vision engineers.

Salary: An entry-level professional can expect an average gross salary of Rs. 500K, as estimated by PayScale.

Eligibility and skills:

1. Bachelor’s/Master’s/PhD in computer vision, computer science, statistics, mathematics, machine learning, or any other related field. As with all machine learning jobs, some companies prioritise skills over degree and anyone is eligible to apply irrespective of their academic field.

2. Experience with using OpenCV.

3. Programming experience with MATLAB, Python, or R.

4. Know-how of machine learning and deep learning algorithms such as CNN and RNN.

5. Good communication skills so that you can convey your process and results to all types of audiences.

5. Data analyst

Data analysts define an abstract business problem and use data to provide answers. If that sounds like your cup of tea, then go ahead, and gobble up other details.

Data analysts use an array of technologies and machine learning can be one of them. They use ML to predict possible future outcomes based on what happened in the past. This is called predictive analytics and can help businesses in discovering hidden insights and drive growth. For example, you can use a customer’s purchase history to determine what they are likely to buy in the future.

Salary: Those who are just starting out can earn Rs. 419K per year which goes up to Rs. 645K when you reach midway in your career.

Eligibility and skills:

1. Bachelor’s or Master’s degree in data science, mathematics, statistics, or computer science.

2. Good understanding of ML algorithms.

3. Hands-on experience in data analysis tools such as R, Tableau, and MS-Excel.

4. Ability to code in Python or R. However, they are not expected to know as much coding as a data scientist or an ML engineer.

5. Knowledge of working with databases such as MySQL.

6. Excellent communication skills. Data analysts are often referred to as data storytellers!

This was an overview of careers in machine learning. If you want to initiate your ML journey, then check out Internshala’s Machine Learning course or Machine Learning Specialization, and get your ticket! You can also use BLOG10 to get a special reader’s discount of 10%.

If you have got the skills, then kick-start your career by checking out these machine learning internships or machine learning jobs on Internshala.

Image credits: <a href=’https://www.freepik.com/vectors/technology’>Technology vector created by upklyak – www.freepik.com</a> and payscale.com