Job After MS in Data Science: 10 Steps to 100% Clarity
CATEGORIES
Job After MS in Data Science: 10 Steps to 100% Clarity
- Lovish Rawal
- April 30, 2023
- 2:29 pm
Did you know? Data science is projected to grow by 15% over the next ten years, with an estimated 11.5 million job openings by 2026 in the US alone.
With such a high demand for data professionals, an MS in Data Science can open up numerous job opportunities in a variety of industries.
As a data scientist, you analyse complex data sets to identify patterns, trends, and insights that inform business decisions.
Moreover, data scientists today are in high demand across healthcare, finance, marketing, and technology sectors.
Pursuing an MS in Data Science in the US can lead to many exciting and lucrative career paths, from becoming a data analyst to a machine learning engineer.
In this blog, we’ll explore 10 steps to 100% clarity about landing a job in the US after an MS in Data Science.
1. Reasons Why Data Science is a Great Career Choice?
- A Growing Demand for Data Scientists: More and more companies are integrating a data-conscious business problem-solving model.
Experts across domains believe data-driven decisions are easier to implement.
Thus, making data a vital ingredient and data scientists one of the most wanted professionals in 2023.
- Impressive pay scales: It is a known fact that data science is booming and data scientists are in demand.
However, inferring from the pay scales below it is safe to say that data science jobs are a well-paying career to venture into.
- 1 Lakh+ Data Science Jobs Listed on Job Portals: With almost every industry using data science, you will find an increasing number of jobs in data science, globally.
For instance, look at the number of jobs listed for a data science opening in the US alone.
Some of the top sectors that are hiring Data Scientists are healthcare, finance, E-commerce and retail, marketing and advertising, Tech and IT, transportation, energy, social media and entertainment, and various Government and public sectors.
2. Checklist to Building a Career in Data Science
Choosing a career in data science can be a rewarding decision for those who are passionate about technology, problem-solving, and data analysis.
Keep in mind that companies, recruiters, and industry professionals lookout for high-quality skills and an adaptable mindset.
Apart from this to succeed as a Data Scientist, you will need to:
- Have a strong educational foundation
- Hone data science technical skills
- Build domain expertise
- Work on cutting-edge technologies and solve complex business problems
- Gain industry exposure
3. Choosing the Right Course to Study Data Science
To pursue data science as a career path, you will need to pursue a course that aligns with your professional aspirations.
To give you a kickstart on choosing the right course for yourself, here are some of the popular data science certifications and courses that are highly valued in the industry:
Some popular certifications of Data Science you can take up are
- Certified Data Scientist (CDS) by the Data Science Society
- Certified Analytics Professional (CAP) by INFORMS
- Google Certifications of Data Science
Some of the popular courses you can take up in the USA for data science are:
The above certifications in the field of data science can also enhance your credentials and demonstrate your expertise to potential employers.
4. Work on Industry-relevant Projects
As you pursue an MS in Data Science from a reputed institute, work on projects that will add value to your profile.
By working on projects that reflect the practical application of data science concepts you can become a valuable asset to companies looking to hire.
For instance, three project types you can include in your CV are – Predictive Modeling: Projects where you have built a predictive model using machine learning algorithms. Data Cleaning and Preprocessing: Projects where you have dealt with messy and unstructured data and applied data cleaning techniques such as missing value imputation, outlier detection, and normalization. Data Visualization: Projects where you have created visualizations to help interpret complex data. This can include projects like creating dashboards, graphs, or charts to present insights. |
5. Gain Internship Experience
Practise! Practise and Practise!
Even if it’s data science, the key to becoming a great data scientist too is to apply and diversify your experience.
Plus, it is important because in an office step-up you witness organised roles and responsibilities of job roles, especially in a newly emergent role such as a data scientist.
Some of the job portals you can find data science jobs and internships are
6. Building a Strong CV
To land your desired job after MS in data science, presenting your skill sets in an organised format is vital.
To do so as a data scientist, there are four important details you need to highlight in your CV:
- Details of the data science certification you pursued
- Data science Projects you worked on
- Data science internships
- Skillsets you’ve developed (Technical and non-technical)
Some of the technical Skillsets that highly-paid Data Scientists in the US possess are:
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You can also highlight the niche you are an expert at.
7. Network, Join University Clubs and Student Communities
Build your network as a Data Scientist by actively attending industry events, participating in data science competitions, and engaging with data science communities of your university, etc.
This will help you reach out to peers, and accumulate recommendations.
8. Join Data Scientist Communities
To effectively network with Data Scientists, join popular data science groups, communities, and Facebook spaces popular in the US.
To name a few you can check out Data Science Society, Kaggle Community, and Data Science Central and Meetup.
9. Prepare for Interviews
To complete your preparation to land on your desired data science job, thoroughly prepare for interviews.
Search up for top data science questions asked to check out what recruiters are expecting from professionals and also, key in the questions about the role you’re applying for and prepare accordingly.
10. Salary of a Data Scientist & Negotiation
By checking every box in the checklist, you qualify as a highly desired asset to any recruiter for a job after MS in Data Science.
Therefore, it is important that you negotiate your earnings depending on the market trends of a Data Scientist’s earnings.
As of 2023, the average base salary for an entry-level Data Scientist in the USA typically falls within the range of $60,000 to $90,000 per year.
But, in high-demand locations for Data Scientists such as Silicon Valley, San Francisco, New York City, and other major tech hubs you can expect six-figure salaries as talented Data Scientists.
Therefore, to pursue an MS in Data Science in the US and fund your studies speak to a today.
Frequently Asked Questions About Job After MS in Data Science
1. What skills are required for a job in data science in the US after an MS?
Ans: Proficiency in programming languages like Python and R, statistical analysis, machine learning, data visualisation, data modelling, database management, and communication skills.
2. How can an Indian student with an MS in data science increase their chances of getting a job in the US?
Ans: Gain practical experience, build a strong portfolio, network with industry professionals, attend career fairs and events, and stay up-to-date with the latest trends and technologies.
3. What are the typical salary ranges for data science jobs in the US?
Ans: The average salary for a data scientist in the US is around $113,300 per year.
4. What are some top companies in the US that hire data science professionals?
Ans: Google, Amazon, Facebook, Apple, Microsoft, IBM, and more. Hiring criteria and requirements vary but typically include a strong background in data science, programming skills, statistical knowledge, and a relevant MS or PhD degree.
5. What are important considerations for an Indian student pursuing an MS in data science in the US?
Ans: Cost of living and tuition fees, availability of financial aid and scholarships, quality of the program and faculty, career services and job opportunities, location and culture of the university, visa requirements, immigration policies, and potential cultural adjustments.
