Are you excited to come to the US to pursue a degree in Machine Learning, Data Science, and AI? Do you want to learn how to get a full-time job in a related field after completing your degree? We are here to answer all your questions and hopefully be able to guide you in the right direction towards your dream career.
Data-related jobs have been ranked #3 in Best Technology Jobs, #8 in Work-Life-Balance and it is also in huge demand in the current market. You can take many different career tracks once you graduate with a degree in the space of Data Science, Machine Learning, and AI. Here are some of the most common Data related roles. Different employers may have their own way of defining these roles in their organization. Some of the common job roles are as follows:
Data Scientists – They work on analyzing large amounts of structured or unstructured data using Machine Learning technologies to gather insights and build some sort of a predictive model. Some of the skills needed for this job are knowledge of Statistics, Probability, SQL, Data Science Algorithms, and coding in R or Python.
Data Engineer – They work on building and maintaining complex data pipelines, assembling large and complex datasets to generate business insights. They enable product teams toward data-driven decision-making and support the rapidly growing and dynamic business demand for data. Data Engineers need experience in system architecture, RDBMS, Big Data, NoSQL, ETL, Data-warehousing Concepts, Database design, etc.
Data Analyst – Uses Statistics and tools (i.e., SQL, R, Excel, Tableau, PoweBI) to identify insights, often creating visualizations. They may need to analyze customer insights, provide recommendations, and create targetted campaigns, and measure the performance of those campaigns. Some companies need Data Analysts to have coding experience while some do not.
Machine Learning Engineer – Machine Learning Engineer roles are an integration of Software Engineer and Data Scientist roles. An MLE has great programming experience and also has expertise in deploying ML code to production. They build ML operations pipelines, research and develop innovative models, and also identify the way to maintain, manage, and retrain the models.
How are the interviews structured for each of these tracks?
Every interview will be very different and the skills you will be tested upon will also vary based on the role, the years of experience, and the company you are interviewing with. Before you start interviewing, you need to make sure your resume is properly formatted and attractive to the recruiters. Our team helps in resume review, editing, and formatting. You can reach out to us by clicking here.
Now getting back to the interviews, I am writing this blog based on my interview experience with over 20 different companies. If you are interested to break into the world of Data, I highly suggest taking a SQL course to brush up on all your SQL. I followed this course to study and brush up on my SQL during my interview preparation. For advanced window functions and other SQL-related knowledge, I followed this free Youtube channel. If you are interested in practicing the SQL you learned, LeetCode, and InterviewQuery is the two websites I had used. For brushing up on Machine Learning, I read this amazing book and made notes for myself. In general, the interviews usually comprise the following rounds:
- HR Screening Round – This is the first step of the interview process. The HR schedules a phone interview to learn more about your work experience or project experience, your motivations to move to this company, and your technical skills. This round will be focused on evaluating what you have written on your resume. Some common questions asked during this round are:
a) Describe your latest technical project.
b) What are some of the most challenging parts of the project and how did you overcome them?
c) Give an example of a new skill you learned recently? How did you apply this technical skill?
d) What are your salary expectations from the role? Why are you motivated to apply to this position?
After this interview, they usually present their feedback to the Hiring managers and if the Hiring manager likes your resume, the feedback and your profile, then you are selected for the second round. - Phone technical Interview- If the previous round went well and HR feels you are fit for the role, you will be moved to a Technical Phone Screen or given an online assessment to take. The technical phone screen varies based on the role you are interviewing for. I had interviewed for a Data Scientist, Machine Learning Engineer, and a Data Engineer roles in the past so I am writing this blog based on my experience. A more detailed blog on each interview process will be coming soon. They can ask a programming question followed by Machine Learning concepts for ML-related roles. For Data Engineer they usually ask you to solve a couple of SQL query questions (Medium level) and ask you more data warehousing, and database optimization-related concepts. For online assessments, it depends on which company you are interviewing with. I had taken the online assessment for Goldman Sachs, Microsoft, ADP, and Amazon. They all have a cut-off score and do not need you to complete 100% correctly. But a senior engineer does review your code to make sure you are not copy-pasting code.
- Final rounds- Once you clear the Phone round or the assessment, the final round interview series will be 4-5 rounds of technical interviews at least. This can all occur in one day or may be split between two days. Even though the majority of the interviews will test your technical skills, they will also have some behavioral and leadership principles kind of questions to also identify the culture fit and personality fit.
No matter what round of interview you are taking, remember that the interviews are a great opportunity for you to network with the interviewer, ask them relevant questions related to the job, and their role and interview them as well. Always have 5-6 questions ready to ask your interviewer as it shows your interest in the position you are applying for.
Hope this blog has been useful for you to learn more about breaking into the world of data-related jobs in the US. In the coming blogs, we will talk about the individualized interview experience of engineers working in different companies.
Disclaimer: None of the links I have shared are sponsored. These are some of the links I have used during my study preparation.
Featured Image Credits: Unsplash
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