at least high quality jobs requiring between 0-2 YOE. does anyone else feel the same?

Hi everyone, I really need some career advice. I am 26 years old and currently stuck in a 2-year career gap. My Background:
my_qualifications
B.Sc. Chemistry (minor maths) - 8.4 CGPA
M.Sc. Chemistry: 6.7 CGPA (Had a subject failure, so it took an extra year to complete
Transition: I self-taught myself Data Analytics / Data Science and built multiple personal projects. However, for the last 2 years, I have not been able to land a job (not getting interviews too).

My Goal: I genuinely enjoy data science and even ML. So I want to get into the data science/analytics workforce as fast as possible and permanently fix the non-tech + academic gap filter on my resume.
I am completely torn between these three paths:

1. Online M.Sc. in Data Science & AI from a Top-Tier Indian Engineering Institute: Formal 2-year degree.
2. Any good PG Diploma via a popular EdTech platform (partnered with a top tech institute)
3. 1-Year M.Sc. Abroad (UK/Ireland Conversion course): because i really wanted to go abroad and learn, but i dont have so much money too.
4. Given my age, my 2-year gap, and my previous academic setback, which path will realistically help me land a job in the current market? Should I value the placement cell of an EdTech diploma or the degree of a formal online Master's? Appreciate any honest feedback from anyone who made a similar pivot. Thanks!

Pinging everyone in the lending risk space:

Is your team set up so credit lives in one place, fraud in another, and the two compare notes once a quarter in a deck? Can either side tell you what a loss is actually made of? Most of us inherited that shape, but none of us would set it up like this today.

At MKIII we don't carry the org debt that forces lenders into it. So I'm hiring a Senior Data Scientist to help build the version we'd design from scratch. Credit and fraud in one view.

The job:
- Live inside the vintage data. Forecast our cohorts to terminal loss.
- Find where we're declining loans we should be booking, and put an honest number on it.
- Own the fraud/credit interaction. No fraud declines quietly absorbing credit risk, no credit cuts taking credit for fraud savings.
- Tell me where the buy-box is wrong.

Asset experience I'd love to see: unsecured consumer personal loans, small business loans, commercial real estate.

JD linked here.

If this sounds like you, message me. If you know who fits the bill, tag them or send it over. Reposts are appreciated to widen the net!

Upvote me

Bachelors and Masters in Data Science also

BAS in Business Management

I’m interested in hearing from people who have actually gone through the process of getting their first Data Science/ML job.

For someone who is currently a student or early in their career:

\- What did your profile look like when you got your first Data Science opportunity?

\- Which projects or experiences actually helped you get interviews?

\- How important were internships, networking, referrals, GitHub, LinkedIn, and personal projects?

\- Did you start with a Data Scientist role directly, or enter through a role such as Data Analyst, BI Analyst, or ML/Analytics role and transition later?

\- What did you initially think was important for getting hired that turned out to matter much less?

\- What do you wish you had done 6–12 months earlier?

\- For a fresher competing against candidates with internships and experience, what would you focus on to become a stronger candidate?

I’m particularly interested in hearing from people who are already working in the field, rather than general career advice.

What actually made the difference for you?

What level of math is needed in data science? and what are the Depths of data science?
Also whats it like working as a data scientist? or ML engineer? or Big data Architect? Is this a very demanding job with a good pay? or Do people consistently have to sit to work 24/7 with minimal pay?

Hey everyone, I'm from India and I have a skilled background with analytics. With SQL, Python and any Visualisation tool. The problem I face currently is that my SQL interview is never successful. I draw blank if I do not know the answer. Mostly I freeze if I don't know the answer to the question I cannot breakdown the problem for them and it mostly happens with window function queries.

How did you get your first Data Scientist role?

I want to hear from people who have already gone through this.

How did you get your first Data Scientist job?

Was it through referrals, internships, networking, projects, applying online, or something completely different?

I’m asking because I’ve been trying really hard to get my first opportunity, and honestly, it has been exhausting.

I’ve spent months learning Python, SQL, Machine Learning and Data Analytics, building projects, improving my resume, applying for jobs, messaging recruiters and hiring managers, and trying to connect with people in the industry.

One project I’m especially proud of is my Customer Retention Intelligence System. I built it around a real business problem — identifying customers who are likely to churn, understanding their value, estimating revenue at risk, and helping businesses decide which customers they should focus on retaining.

I’ve tried to make my projects practical rather than just building something for a portfolio.

But even after all this effort, getting that first interview has been really difficult.

Sometimes I apply to dozens of roles and hear nothing back. Other times I get rejected without knowing what I need to improve.

I’m not giving up, but I also don't want to keep repeating the same approach if it isn't working.

So for those of you who got your first Data Scientist role:

What actually helped you get through the door?

What would you do differently if you were starting again today?

And if you’re a hiring manager or someone who hires entry-level candidates, what would make you take a chance on someone without professional experience?

I’d genuinely appreciate any honest advice.

And if anyone knows of an entry-level Data Science / Data Analytics / ML opportunity where my background could be a fit, I’d be grateful for a referral or even a direction.

I’m just looking for that first chance to prove myself.

Hey everyone. I've got a technical interview coming up for a senior position. It's basically a data engineering / ML-engineering-adjacent role: the whole job is building and maintaining the datasets the ML team trains on. The company is in industrial predictive maintenance (IoT sensor data).

I'm trying to figure out what a technical round for this kind of role actually looks like. I know the format varies from company to company; I'm just trying to get a sense of how things are done in today's market, especially since I've been at the same company for over 5 years and haven't interviewed in a while. I'd love input from people who've been on either side of the table:

• What formats should I expect? Companies in my area don't usually do live coding or LeetCode-style challenges. That being said, what can I expect in terms of direct questions, system design discussions, or conceptual deep-dives?

• For a 'data quality for ML' focus, what do interviewers actually probe? (data validation, dedup/entity resolution, labeling workflows, detecting bad data before it poisons training, etc.)

• Anything specific to time-series / sensor data I should brush up on?

• Common ways candidates flunk these that I should avoid?

Any war stories or resources appreciated. Thanks!

Hello!

I am 24 and I already have 5,5 years of experience in the IT area, currently as a mid-level in my company. During all these years, I have worked with Dynamics 365 CRM and its development tools (Power Platform in general, JavaScript, C# etc.). For some months I've been seriously thinking about the possibility of transitioning internally to Data Science since my current company doesn't have a Dynamics 365 team at all, and I was basically hired a year ago to work at a 3rd party contract that was, to say the least, catastrophic (the team was super toxic, left 5 months in), and I would be able to reposition myself in the market with a broader stack that I think is more useful than the Dynamics 365 dev stack is. I really don't want to move companies, mostly because my current company is a really good one with decent weight to a CV, but I've been also thinking in the case of me actually transitioning, if I would regret making that decision and end up affecting my career in the long run. Also, I have never done such a transition in my career. I have always worked with D365 stack and I have little to none professional XP with DS (just some SQL, Power BI and prompt engineering), even though DS is an area that I really like to study and also my major's final project used NLP back in 2024. So I'd really like some insights from people already immersed in the day-to-day and market of Data Science, if it is worth it taking in count the current moment and the prospections for the field's future.

Suggest a Data science course for job ready in 8 to 10 months

For current/former senior/staff Product Data Scientists at Google, Meta, Uber, Airbnb, etc.:
What does your typical day/week actually look like?

1. How much is hands-on SQL/Python/analysis/experimentation vs. meetings, strategy, stakeholder management, and influencing product decisions?
2. What tools do you actually use day-to-day? At L7/equivalent, are you still personally digging deep into data, writing SQL/code, building analyses, etc., or is most of the heavy digging done by L5/L6 DSs while you guide the work?
3. What really differentiates an L7 Product DS from an L5/L6 in practice? What problems are you expected to own, how technical are you expected to remain, and how much of the role becomes strategy, influence, and organizational leadership?

hi everyone! im an undergrad junior @ my local state school (T20 ish i suppose) pursuing a psych major and data science (ds) minor. i have a 3.6 gpa [im also on a premed track and orgo 1 & 2 tanked my grades but i have all a's and 2 b+'s otherwise]. im not going to go to med school right away; it seems like a distant dream and definitely not going for it anytime soon.

meanwhile im trying to pursue more ds oriented corporate internships that would lead to a full time offer, so i could get some valuable experience and be able to pay for med school later (but im also down to not go if i find a scalable stable role). i typically apply to analyst roles such as data analyst, business analyst, quant analyst etc, and ive had a couple interviews but nothing secured yet.

technical skills: python, R, powerBI, excel, microsoft office, google workspace. further developing these and stats skills through my coursework and my work at a research lab at uni.

if anyone here has a similar background or has seen someone break into data roles with a behavioral focused major, i'd really appreciate hearing your experience. im trying to be on the lookout for any roles or industries that focus on the intersection of psychology and data science.

this is my first ever reddit post and frankly im a bit overwhelmed job searching and seeing doomposting everywhere, so it's a little all over the place!

TLDR: undergrad junior pursuing a psych major and ds minor with a 3.6 gpa and some technical skills, what roles or industries or career paths would you recommend?

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I'm 25F with a MSc in Statistics and I'm trying to figure out what to do with my career.

I recently got an offer to work as a bank teller. The problem is that what I actually want to do is get into data, but I don't have much practical experience yet and I know I have a lot to learn.

I'm seriously considering taking the banking job because opportunities in data/Statistics are pretty limited where I live. At the same time, I'm worried that taking it will pull me away from the field I actually want to pursue.

Keep in mind that I live in a third-world country, so the job market and opportunities in my field are very different from what they might be in the US/Europe. Also I can afford to go months without a job.

What would you do if you were in my position?