10 Tips to become a Data Scientist or Machine Learning Engineer

10 Tips to become a Data Scientist or Machine Learning Engineer

Play this article

Data Science and Machine Learning have been gaining a lot of traction in the recent decade. A large number of startups and highly established companies are moving toward the field of artificial intelligence to further strengthen their products with data-related capabilities. Furthermore, there are quite a large number of postings for jobs that require a specified set of skills to solve the problems. Hence, comes the positions of data scientists or machine learning engineers who are involved in churning the data and finding useful patterns along with making use of ML models to generate real-time predictions. Companies are looking for the right talent but are not able to do so despite a large number of courses (Masters or Ph.D.) in this field. This explains why there is a high demand for data scientists and machine learning engineers.

Below are some of the tips that I would suggest to anyone who is planning to enter the field of data and churn it to become data scientist or machine learning engineer. Going through these steps can highly influence the success rate of your application.

Tips to Become Data Scientists or Machine Learning Engineers

We are going to be looking at various tips that can help someone who is new to machine learning to gain data-related positions such as data scientists or machine learning engineers.

  • Speak to Data Scientists — You heard it right. In order to be more knowledgeable in the field of data science, it can be most useful to have conversations with people who are experienced in the field. Learning about various strengths and weaknesses of ML models along with discussing what not to do during data science workflows is useful for an aspiring data scientist. They can better guide you in the overall process of how data is leveraged and used for various purposes such as exploratory data analysis, deep learning, machine learning, and many other subsets of artificial intelligence.
  • Build a Strong Portfolio — For those of you who are new to data science and machine learning, doing a large number of online courses can help in building a strong theoretical foundation. However, it is also important to apply the learned theories practically by building the projects and uploading them either on your public website or on GitHub. A lot of recruiters want to look for your proof of work and how you would be able to handle their data-related situations. In such a case, building and highlighting a list of your projects from GitHub can be handy and useful for a large set of reasons. There is a higher chance of getting calls from recruiters from a vast array of companies. For those of you who are looking forward to building a strong portfolio, feel free to take a look at my GitHub portfolio. Below is the link.
  • Polish your Resume — While this can sound obvious, taking care of your resume and highlighting your major accomplishments can be handy for aspiring data scientists. Try to ensure that you mention the tools that were used for the position to which you are applying and also, add more projects if you lack a bit of experience that is typically needed for a data scientist-related role. There are some formats that are quite attractive and catch the eye of the recruiters. Feel free to research from Google and Bing about various resume formats that clearly highlight your skills.
  • Have Good Communication Skills — Machine Learning and artificial intelligence is all about communicating the outcomes to a large audience of them including data engineers, managers, machine learning engineers, and software developers. During the interview, therefore, interviewers expect candidates to have good communication skills. Enrolling in a few online courses can help build communication skills to a large extent. Furthermore, devoting a good amount of time to improving your communication skills daily would have a great impact on your interview experience.

-Gain a Lot of Theoretical Understanding — There is a lot of mathematics involved in machine learning and data science. Getting your hands dirty by solving some of the optimization problems or looking for ways to improve the model by altering a set of parameters can help you to know the intricacies involved in machine learning when using mathematics under the hood. Having a good theory behind the working of various machine learning models ensures that one understands how they work internally before they perform operations such as optimization and hyperparameter tuning to build interesting models. Going through a set of online courses helps one learn the theory quite well because of the vast array of problems that they teach to solve well. Therefore, I would suggest improving your theoretical understanding of machine learning and data science concepts to increase your chances of becoming a data scientist.

-Learn the Key Programming Languages — Now that you’ve learned the theory behind machine learning and artificial intelligence, it is now time to also practically implement those concepts in real-life. After all, it is the practical application that leads to the business turning heads towards your work and its appraisal. Some of the languages that are most often used by data scientists or machine learning engineers are Python and R. There are other tools such as SAS but the above two are used quite often. I would suggest learning Python as it is quite easier to learn (syntax is easy) and used not just for machine learning but also for easier web development and software engineering. On the other hand, R is mostly used for statistical analysis and by researchers who explore the data and algorithms before designing the final architecture to be published in research papers.

Link to learn Python from: https://app.techlearnindia.com/python-basics-data-expert/?coupon=Global50

  • Advertise Yourself on Social Media — There are a large number of social media platforms such as LinkedIn (Facebook as well) that help you advertise your skillsets to be fulfilled for machine learning and data science-related jobs. Constantly updating your profile and staying in touch with recruiters so that you would be the first person they would be reaching out to if they find job openings that you would be a strong fit. Adding quite a lot of posts on LinkedIn also ensures that you are targeting a large audience and chances are that someone from a prestigious company recognizes the work before their recruiter reaches out for potential interviews. I would suggest using LinkedIn to your advantage by publishing your work and showcasing your portfolio.
  • Work on Projects Outside Your Curriculum — This is especially true for students who are currently pursuing engineering or a master’s in the field of data science or artificial intelligence. A lot of students get caught up in completing the assignments at the last minute before understanding the concepts in great detail. The content from these topics could be memorized quite easily when you are doing side projects that use these concepts. Therefore, I would strongly recommend those who are new to machine learning to also build side projects and also upload them to various websites or platforms where the work is visible quite well. Recruiters are constantly looking for talent in this field and if they find your profile interesting, especially based on the side projects, they are more likely to arrange calls with their team members for selection.
  • Prepare for Interviews — While this suggestion can sound intuitive, there are a lot of candidates who fail to know the questions that are usually asked in a data science-related position. During that time, it would become somewhat hard or difficult for them to crack the interview, considering the complexity of the questions asked. Therefore, one must take a good amount of time to prepare for interviews which would help strengthen the profile to a large extent. After giving a solid performance during the interview rounds, there is security assured for the candidate as he/she is most likely to get placed. Therefore, a good amount of time must be spent on the preparation for the data scientist or machine learning engineer role.
  • Earn Certifications — When you look online for data-related courses, you would mostly come across a large number of them being in the field of data science and machine learning. There is a website that would in fact help build a strong theoretical understanding of machine learning fundamentals. After going through these courses and earning a certification, it would be also a good idea to post it in your portfolio to show credibility to your profile. Recruiters and hiring managers would be keen on adding to their team if they see that you are qualified and certified for the job. Therefore, it would be a great thing to add your certifications to your portfolio as it would increase your chances of getting selected.

Link of that website is: https://techlearnindia.com/#/job-profile-courses

Conclusion

After going through this article, I hope that you got a good understanding of the things that would in fact help you become a data scientist or a machine learning engineer. Ensuring that sufficient time is used for building your profile can be a great deal especially if you want to become a data scientist or a machine learning engineer. After reading this article, hope you become a great data scientist or a machine learning engineer.

Did you find this article valuable?

Support Techlearnindia by becoming a sponsor. Any amount is appreciated!