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Hello, I'm TJ!
Recently, there has been a significant increase in people wanting to study in technology fields and work in the United States. For such individuals, I highly recommend graduate programs in Data Science and Business Analytics.
Graduates from these programs earn an average annual salary of over $100,000 in their first year after graduation, with income increasing further as they gain experience. Particularly in major tech companies and the financial sector, it's not uncommon for total compensation to exceed $200,000 when including stock options!
Additionally, most of these specialized fields are STEM-designated, allowing international students to work in the US for up to 3 years after graduation through OPT (Optional Practical Training). In fact, MIT's Master's in Business Analytics reports that 98% of graduates secure employment in the US, demonstrating high employment rates even for international students.
This article provides detailed information about hiring trends, career paths, salary levels, and recommended graduate programs for Data Science and Business Analytics positions in the United States. If you're interested in a tech career, aiming to increase your income, or considering employment in the US, please read on until the end!
1. Data Science & Business Analytics Employment Trends in the United States
Rapidly Growing Job Market Across Diverse Industries
The demand for data science professionals in the United States remains high, with continued growth expected. According to the Bureau of Labor Statistics (BLS) projections, employment for data scientists is expected to increase by 36% from 2023 to 2033, classified as "much faster than average" growth.
Similarly, Operations Research Analysts, which overlap with business analytics roles, are projected to see 23% employment growth. This significantly exceeds the average for all occupations (around 4%), confirming the ongoing strong market for data scientist positions. While large-scale layoffs occurred across the tech industry around 2023, causing a temporary slowdown in hiring, job openings rebounded in 2024 with a substantial increase from the previous year.
For example, as of August 2024, data science job postings increased by 130% year-over-year, with growing demand also reported for AI researchers and machine learning engineers. Overall, the market for data professionals continues to expand, with skill requirements becoming more sophisticated alongside the latest AI boom (discussed later).
Industries Actively Hiring
The need for data scientists and analytical talent extends beyond tech companies to various industries. Job market analysis shows that approximately 30% of job postings come from the technology and engineering sector, making it the largest employer. This is followed by recruitment and HR (19%), healthcare and life sciences (13%), finance and professional services (10%), and materials and manufacturing (about 9%). Currently, professionals with data analysis skills are in demand across virtually all industries.
Looking at specific recruiting companies, major tech firms including Google, Amazon, and Meta (Facebook), financial giants (J.P. Morgan, Goldman Sachs, Capital One), consulting firms (McKinsey, BCG), and healthcare companies (including pharmaceutical companies like Pfizer) are all actively hiring data science professionals.
For instance, examining where graduates from MIT's analytics program find employment reveals positions at IT companies like Amazon, Meta, and TikTok; major consultancies like McKinsey and BCG; and pharmaceutical companies such as Sanofi and Pfizer, demonstrating cross-industry opportunities. In the financial sector, Morgan Stanley and Wells Fargo, along with insurance and fintech companies, are recruiting numerous data analyst positions.
Additionally, retail and logistics companies like Walmart and Whole Foods, and manufacturers such as BMW and General Motors are hiring specialized talent to leverage data analysis in their business operations. This highlights how "data utilization" has become a critical focus across all industries, creating widespread demand for skilled professionals.
Diversification of Job Titles and Positions
Job titles for data-related roles have become increasingly specialized in recent years. While "Data Scientist" is a representative position, recruitment occurs under various titles depending on business needs.
For example, there are "Data Analysts" who perform more business-oriented data analysis, "Data Engineers" who build and manage data infrastructure, "Machine Learning Engineers" who specialize in implementing machine learning models, and "Business Intelligence (BI) Analysts/Engineers" who visualize management metrics and conduct internal analysis.
Furthermore, companies may create unique position titles such as "Data Science Consultant," "Data Product Manager," or "AI Research Scientist." Although the core skill sets often overlap across job requirements, it's important to understand that titles are diversifying according to roles within organizations.
2. Data Science Graduate Programs and Their Impact on Careers
Comprehensive Programs at Top Universities
The United States offers numerous highly-regarded graduate programs in data science and business analytics that provide strong career advancement opportunities for international students.
In recent rankings, MIT's Master of Business Analytics program ranks #1 in the QS World Rankings, followed by UCLA's Master of Business Analytics at #2. Other consistently highly-rated programs include Stanford University and Carnegie Mellon University's data science-related programs, Columbia University and New York University's Master's in Data Science, and Harvard University's Master's in Data Science (statistics-focused).
Interestingly, Fortune magazine's 2025 evaluation placed Harvard University at #1 for in-person data science master's programs, with public institutions like Oklahoma State University and Michigan State University also ranking in the top 3, demonstrating that diverse universities are achieving excellent outcomes. While "prestigious universities = advantage" generally holds true, there are many other programs with practical curricula, making it important to select a university that aligns with your specific goals.
Program Content and Corporate Partnerships
Top-tier programs offer cutting-edge curricula and maintain strong industry connections. For example, MIT Sloan's Master of Business Analytics is an intensive one-year program that includes a seven-month data analysis project with companies (Analytics Capstone) where students work in teams to solve real business challenges.
Past capstone partners include BMW and GM (automotive), Anheuser-Busch and Unilever (consumer goods), and Pfizer (pharmaceutical), with internships and final presentations occurring at the end of the program. Companies pay a sponsorship fee of $22,000 per student, allowing students to offset some tuition costs while gaining valuable practical experience and networking opportunities.
As a result, this program boasts exceptional employment outcomes, with the 2021 graduating class achieving a 100% placement rate within six months of graduation (based on job seekers). Graduates found positions across various industries: technology (41%), consulting (30%), finance (11%), healthcare/pharmaceuticals (5%), and retail/consumer goods (5%). Notably, 98% of graduates secured employment within the United States. Furthermore, this program is STEM-designated, allowing international students to work in the US for up to 3 years after graduation through OPT, making these graduates attractive to employers (STEM benefits discussed later).
Beyond MIT, Columbia University's MS in Business Analytics (offered by the IEOR department of the engineering school) is a 1.5-year STEM program featuring advanced statistics and machine learning curriculum along with robust industry collaborative projects and career support.
The December 2023 graduating class achieved a 96% employment rate (three months post-graduation), with virtually all job seekers finding positions. The industry breakdown shows finance (39%) as the most common destination, followed by technology (approximately 30%), consulting (8%), and healthcare (6%), reflecting the program's New York City location advantage in financial sectors.
These universities also provide comprehensive career services, including corporate presentations and recruiting events, giving students access to internship and job opportunities during their studies. In fact, Columbia's Data Science Institute (DSI) reports that "major companies are eager to hire graduates immediately, resulting in full employment for past graduates."
Overall, enrollment in highly-rated programs not only facilitates the acquisition of cutting-edge skills but also provides access to powerful employment networks, creating a significant advantage for international students pursuing US employment through OPT.
STEM Designation and OPT Extension Benefits for International Students
Most data science and business analytics programs are classified as STEM fields (Science, Technology, Engineering, and Mathematics), allowing F-1 visa students to qualify for OPT (Optional Practical Training) for up to 3 years (12 months + 24-month extension) after graduation. This is a crucial factor in improving employment rates for international students in the United States.
Some general management degrees, like certain MBA programs, are not STEM-designated, limiting OPT to just 1 year without extension options. This means students must secure work visas within this timeframe or return to their home countries. MIT representatives have noted that "companies tend to hesitate when hiring international students with only 1 year of work authorization, but are more willing to hire those who can work for 3 years."
In contrast, graduates from STEM-designated analytics programs have longer periods to work in the United States, making them more attractive to employers, which explains why 98% of MIT graduates secure positions in the US. Government statistics also show that the number of international graduate students using STEM-OPT has increased yearly, with 122,101 individuals utilizing STEM-OPT to work at US companies in 2023 (a 37% increase compared to 2017).
Therefore, when selecting a graduate program, it is extremely important to verify whether the program has STEM designation and to prioritize those with strong career support and industry partnerships, as these factors significantly impact the likelihood of securing positions during the OPT period.
3. Salary Ranges and Regional Differences for Data-Related Positions
Overall High Income Trend
Data science and analytics positions are known as some of the highest-paying roles in the United States. According to BLS surveys, the national median annual salary for data scientists in 2023 was approximately $108,020 (about 14.4 million yen), significantly higher than the average across all occupations.
Similarly, operations research analysts, a type of data analysis position, also have a high median salary of $83,640. However, there is considerable variation based on industry, region, and years of experience, resulting in diverse actual salary ranges.
Private surveys report average annual salaries for data scientist positions ranging from approximately $132,000 to $190,000, and at major tech companies, total compensation including stock options often exceeds $200,000.
Average Salary by Position
Average Salaries for Data Positions in the US
*Note: Salaries increase significantly with experience (e.g., Data Scientists with 10+ years of experience earn approximately $189,000)
*Note: "Data Analyst" and "BI Analyst" positions tend to be on the lower end, while "Data Architect" positions tend to be higher
*Note: Regional differences are substantial, with salaries in major cities like San Francisco and New York typically 20-30% higher
For data scientists, for example, salaries increase significantly with experience — from approximately $117,000 for junior positions (0-1 years of experience) to approximately $189,000 for mid-senior levels (10+ years).
Additionally, analysis-focused positions like "Data Analyst" and "BI Analyst" tend to offer somewhat lower compensation than technical specialist roles, while positions requiring advanced design skills, such as "Data Architect," typically command higher salaries.
Regional Differences
Salary variations by region are also notable in the United States, with coastal cities having higher cost of living offering compensation well above the national average. For example, the average annual salary for data scientists in the San Francisco metropolitan area is approximately $145,000 plus $34,000 in bonuses (totaling around $179,000), while New York offers approximately $160,000 in total compensation.
In comparison, cities like Chicago and Denver report around $140,000, while the national average is estimated at approximately $126,000. Therefore, working in major cities on the West or East Coast (Silicon Valley, NYC, Boston, etc.) typically results in salaries 20-30% higher than those in Midwest or Southern cities. While cost of living must be considered, pursuing employment in urban areas with concentrations of tech companies is advantageous for those seeking higher compensation.
Industry Differences
Salary levels also vary by industry. Generally, technology (IT), telecommunications, insurance, and financial services industries offer higher compensation for data science positions, with average annual salaries reported to reach around $160,000.
In contrast, education and manufacturing sectors tend to offer somewhat lower salaries, averaging around $120,000. For example, the financial industry secures talent with attractive compensation packages as data analysis directly impacts business strategy, while traditional manufacturing tends to offer relatively lower compensation for IT personnel. However, with the increasing adoption of IoT and AI in manufacturing, salary levels in these industries may rise in the future. Overall, the pattern shows that "data professionals earn high salaries across all industries, but tech and financial sectors offer particularly high compensation."
4. Positions and Strategies for International Students Seeking Employment in the US
Maximizing STEM Qualification and OPT Extension
For international students pursuing employment in the United States, the first priority is securing sufficient legal work authorization after graduation.
Fortunately, most data science and business analytics degrees are STEM-designated, allowing for up to 36 months (3 years) of OPT after graduation. This 3-year grace period offers three chances to obtain an H-1B work visa, providing employers with confidence that employment won't terminate prematurely.
In contrast, non-STEM degrees (like many MBA programs) only offer 1 year of OPT, which may discourage companies from hiring due to visa lottery uncertainties. Therefore, international students should choose STEM programs whenever possible to secure this OPT extension eligibility.
Additionally, students should ensure timely and accurate OPT application and extension procedures with support from their university's international student office to maintain continuous work authorization. The fact that over 120,000 international students were working in the US through STEM-OPT as of 2023 demonstrates how widely this program is utilized by international talent.
Identifying Accessible Positions and Industries
Positions relatively open to international students include specialized roles requiring advanced technical skills. In the data science field, roles such as data scientist, data engineer, and machine learning engineer tend to have many companies willing to offer visa sponsorship due to chronic talent shortages.
In the US, positions requiring specialized knowledge qualify as "Specialty Occupations," making H-1B visa acquisition more accessible, meaning qualified international students have better chances of being hired (the prevalence of "master's degree required" job postings also works in favor of international students). In particular, IT companies, financial institutions, and consulting firms have extensive experience hiring international talent and sponsoring visas, with numerous international students receiving offers from these companies each year.
Major IT companies like Amazon, Google, and Meta apply for hundreds of H-1B visas annually, including for analytical positions. Similarly, the finance and insurance sectors actively recruit international students as data analysts and quantitative specialists. However, positions requiring security clearance, such as those in government agencies or defense-related fields, may be off-limits to foreign nationals and should be avoided. US subsidiaries of international companies can also be options, though prior research is necessary as some may have limited visa sponsorship experience.
Importance of Internships and Networking
For successful employment in the United States, internship experience is extremely important. Summer internships during graduate studies or company projects (such as the MIT Capstone mentioned earlier) provide not only practical experience but also significant opportunities to receive full-time employment offers.
Statistically, especially in consulting and finance, conversion from internships is the primary recruitment pathway for new graduates. International students need to obtain CPT (Curricular Practical Training) authorization for internships during their studies, which can be arranged smoothly with proper guidance from their university.
Participating in a summer internship and securing a full-time offer in the fall creates an ideal trajectory: beginning OPT employment immediately after graduation followed by H-1B application. Beyond internships, building connections through university career fairs and alumni networks is crucial. Particularly, connecting with alumni from your home country can provide valuable information and insights.
In the US job market, proactive outreach is valued, and obtaining internal referrals through networking can give you an advantage in the application screening process.
Identifying Visa Sponsoring Companies
When selecting target companies, it's important to research their track record of hiring international candidates and providing visa sponsorship. While multinational and large companies are generally more open to sponsorship, startups and smaller businesses might be reluctant.
With the recent growth of remote work, some flexible companies now offer remote contracts if visa acquisition proves difficult. However, if your goal is to continue working while residing in America, it's safer to choose companies that will reliably help you transition to an H-1B or other work visa (such as O-1).
Although H-1B visa competition has increased with more applicants being rejected in the lottery, a longer OPT period allows for multiple attempts in subsequent years. Even if unsuccessful in the lottery, some sponsoring companies may offer reassignment to their offices in your home country with possibilities for future return to the US. Considering such risk management options, developing a strategic career plan is crucial.
Overall, successful employment strategies for international students, including Japanese nationals, include: (1) acquiring specialized skills through STEM programs to secure OPT, (2) gaining US work experience through internships, and (3) targeting companies with sponsorship track records while leveraging networks to secure offers.
5. Strengths and Weaknesses Compared to MBA Programs (Data Specialization vs MBA)
Differences in Curriculum and Skill Sets
Specialized master's degrees in Data Science/Business Analytics (MS) and Master of Business Administration (MBA) programs develop different skills and career paths.
MBA programs focus on general management and organizational leadership, with coursework including accounting, economics, business strategy, marketing, and organizational behavior.
In contrast, MS in Business Analytics and MS in Data Science programs specialize in data analysis methods and tools, centering on technical subjects like programming (Python, SQL, R), machine learning, statistical modeling, optimization techniques, and data visualization.
Consequently, MBAs develop "business leadership skills for leveraging data" while MS programs cultivate "specialized expertise to independently execute advanced analysis and advise management". A Wake Forest University professor succinctly states that "MBAs train business leaders with analytical knowledge, while MSBAs produce professionals with deep analytical expertise who can propose data-driven decisions to leadership".
Advantages in Early Career Stages
For those aiming to enter data-related positions as new graduates or early-career professionals, MS degrees offer more direct advantages by providing specialized skills. Hiring managers often note that "MBA graduates with some analytical coursework aren't immediately effective in analytical roles, while MSBA graduates are far more practically prepared."
Indeed, many companies prioritize master's (MS) or doctoral degrees in relevant fields, or equivalent practical experience over MBAs for data scientist and analyst positions. As mentioned earlier, most MS programs' STEM designation allowing OPT extensions is a significant advantage for international students (while traditional two-year MBA programs often don't qualify for OPT extensions).
MBA graduates have limited specialized data skills upon graduation, making them less competitive when applying directly for pure data science positions, with some exceptions like data strategy consultants. However, MBA holders might be hired as potential managers to lead analytics teams or leverage their previous domain knowledge plus MBA education to work in roles such as business improvement consultants.
Essentially, there's a distinction: "Choose an MS if you want to start as a technical specialist; choose an MBA if you want to develop versatile business skills and aim for management positions in the future."
Future Career Paths and Income
Over the long term, MBA and MS graduates tend to follow different career trajectories. MBA graduates, as generalists, have advantages in advancing to management and executive positions, opening pathways to high-paying management roles such as Marketing Manager (average annual salary $166,000) or Finance Manager (average $175,000).
In fact, GMAC (Graduate Management Admission Council) surveys report that 90% of companies plan to hire MBA graduates, confirming that MBA remains the traditional route for leadership talent. MS graduates, with their highly specialized initial positions, may sometimes need additional business experience for advancement.
However, as data strategy has become key to corporate competitiveness, more data science professionals are being promoted to senior management. With increasing demand for "hybrid talent" proficient in both technology and business, MS graduates have expanding opportunities to advance to manager and executive positions as they gain experience.
If they feel their business knowledge is insufficient, they can consider pursuing an Executive MBA later in their careers.
Different Roles Within Organizations
MBA and MS graduates often serve different roles after joining companies. According to Wake Forest University's comparison, MBA graduates (including those with Business Analytics concentrations) typically take positions that leverage data for business decision-making, such as consultants, BI analysts, marketing managers, and business development managers.
Meanwhile, MSBA graduates tend to fill positions involving actual data analysis and model development, such as data scientists, data analysts, machine learning specialists, and risk modeling specialists. The two groups have a collaborative relationship within organizations, ideally forming a complementary partnership where MBA-educated managers define business challenges and MS-educated data specialists provide analytical solutions.
While recent trends show MBA programs strengthening analytical skills education and MS programs incorporating leadership training, distinct areas of expertise remain clear. Therefore, it's advisable to choose between MBA and MS based on whether your preference is to be "in a position to make management decisions armed with data" or "in a position to provide value as a data analysis specialist."
Summary
As the trend toward data-driven decision making strengthens, the importance of professionals with advanced data skills continues to increase. Therefore, for early-career professionals with a specialist orientation, obtaining an MS in a data science field offers significant benefits. Simultaneously, a broad business perspective and management abilities remain important, and MBA holders continue to occupy decision-making positions in large organizations.
Ultimately, aligning your educational choices with your career goals to leverage the strengths of both paths is essential. For example, you might consider a two-stage strategy: "First develop data expertise through an MS and begin your career as a data scientist, then obtain an MBA later to advance to management positions."
In the US business world, it's important to remember that degrees are just one of many career tools, and results and experience ultimately matter most. Ideally, you should aim to become a professional who can integrate and apply skills from both domains in practical settings.
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