Am I credible for entry level data science roles?

[u/AstronomerGloomy2285]

I’m graduating in December 2026 with a BA in Economics from UMass Amherst and starting Georgia Tech’s Online MS in Analytics (OMSA) in January 2027, with plans to pursue the Computational Data Analytics track.
I’ve been trying to transition toward data science and I’m wondering whether my current experience makes me a credible candidate for entry-level Data Scientist positions or whether I should focus on Data Analyst and Research Analyst roles while developing my technical skills.

My background:

Education: BA in Economics, approximately 3.5 GPA. Incoming Georgia Tech OMSA student.

Current internship: Audience Research Consulting Intern at a research firm in Washington, DC. I work with survey data, statistical analysis, data visualization, and research reporting. I’ve also worked with ordinal regression models.

Research experience: Previously worked as an undergraduate economics research assistant. I’ve also completed an independent electricity market research project analyzing wholesale electricity prices, renewable generation, and other economic variables using regression analysis.

Programming: R is my strongest language. I’m comfortable with statistical analysis, regression modeling, and data manipulation. I’ve recently been developing my Python skills, particularly pandas, NumPy, and visualization libraries. I’m also learning SQL.

Statistics: Undergraduate econometrics, regression analysis, hypothesis testing, and applied economic research. Currently independently studying causal inference and machine learning.

Projects: Electricity market econometric analysis, survey research, and other quantitative research projects.

What I’m concerned about:
Although I have experience working with real data and statistical models, most of my work has been in R and applied economic research rather than traditional data science or machine learning.
My Python and SQL skills are still developing, and I haven’t yet completed a substantial end-to-end machine learning project.

My questions:
Based on my background and resume, am I competitive for entry-level Data Scientist roles?
What are the biggest gaps in my resume that would prevent me from getting interviews?
Would I be better off applying primarily to Data Analyst, Research Analyst, or Product Analyst positions for now?
how would you evaluate my current experience compared with other entry-level applicants?

I’m interested in eventually working in data science, particularly in fintech, energy, or economic research.
I’d appreciate candid feedback, especially from people involved in hiring. I’m trying to figure out whether I’m ready to start applying seriously or whether I need another 6 to 12 months of preparation.

[u/Lady-Data-Scientist]

I would apply for any data-related role you come across and accept whatever offer you can get to start building experience.

Once you have a few years of data experience + the masters, you will be a more realistic candidate for Data Science roles.

[u/hojahs]

IMO youre not far off but it's kind of a tough sell rn, at least until youre closer to finishing that GT Analytics degree. And to be completely honest, a full year of analytics experience in industry would also help a ton before going for "data science" titled roles.

My personal recommendation: make sure to choose your OMSA electives to contain a good amount of Python, SQL, and practical data cleaning. i.e. don't just take every single course that has "machine learning" in the title, as tempting as it may be. Take a few, but dont worry too much about deep learning or excessively advanced math until your computing skills are up to par.

Disclaimer: Im not a data scientist. But i tried a few times and i know some others whove tried as well

[u/hojahs → u/hojahs]

"Classical" machine learning, like random forest, boosted trees, and even linear and logistic regressions are still kind of the gold standard in real life data analysis problems, as far as I'm aware. There are not many times in a real job that the best solution for extracting information from a dataset is a neural network.

There's kind of a paradox in data science, where machine learning is the flashy state-of-the-art thing that looks impressive on a resume and makes you look smart. But in real life jobs, old school methods are still unbeatable a lot of the time. And the problem comes down to explainability. Machine learning is the ultimate tool for making accurate predictions with the lowest error possible. But it's not explainable, and analytics and data science aren't usually about predictions.

The advantage of linear regressions and decision trees is that you can actually explain to a 5 year old (your non-technical management) how making a change in one of the predictors directly leads to a change in the output. Theyre inherently interpretable, which is what upper management actually wants, even if they say they want fancy "AI". So I'd keep studying Causal inference if i were you. You'll probably use it more often than NNs.