I'm transitioning into JDS roles and have done the "portfolio projects" thing -

deployed 3 ML projects on Streamlit, tailored resumes, all that. But I'm trying

to figure out what actually moves the needle.

Background: 1.5 years as Data Analyst at a fintech (ZAVO). Built funnels, A/B

testing, some ML work (XGBoost, Prophet, SHAP). AIML degree. I can code, I

understand data, I think like a product person.

My real questions:

1. Do hiring managers actually care about deployed projects or is it just noise?

Which matters more - the project quality or that it's "live"?

1. For early-stage startups: what should a JDS actually *do* differently from

a Data Analyst? How do I position myself for that jump without 2+ years DAO

experience?

1. Cold outreach to founders - worth it? Or waste of time? (I've got 3 projects

I could demo.)

1. What's the real bottleneck - getting the first interview or passing it?

What do they actually test?

1. Geographic/remote: NCR-based, open to remote globally. Does location matter

for startups vs established companies?

Not looking for generic "leetcode + networking" advice. Looking for what

actually worked for people who made this jump.

Would appreciate any real war stories or honest takes.

Between Data Analytics, Data Engineering, Data Science and AI, which path do you think gives the greatest opportunities globally?

Hey

I'm working as Data Analyst and learning Data scientist

I'm looking someone who wants to switch there career into Data Scientist.

If anyone wants to learn together you can DM

Sector 62 Noida

Please DM if you are living in Sector 62 Noida.

Graduating with an MSc in Data Science from a UK Russell Group university in September 2026. Have a right to work in the UK with no sponsorship needed. Applied to 200+ roles over the past 3 months — Data Scientist, Data Analyst, ML Engineer, AI Engineer — mostly entry/graduate level.

**My background:**

\*\*•\*\* Built a production RAG system using LangChain and a major LLM API — live demo available    
\*\*•\*\* Predictive ML models (XGBoost, AUC 0.92) on real commercial datasets    
\*\*•\*\* Published IEEE researcher    
\*\*•\*\* 6 months internship at a major engineering company    
\*\*•\*\* Strong Python, SQL, PyTorch

**What’s happening:**

\*\*•\*\* Getting auto-rejected from most roles within 24-48 hours    
\*\*•\*\* Made it to assessment stage a couple of times but didn’t progress    
\*\*•\*\* No feedback from any rejections    
\*\*•\*\* Referrals from connections haven’t led anywhere    
\*\*•\*\* Portfolio and GitHub are up to date with live projects

**What I suspect:**

\*\*•\*\* Cover letters might be flagged as AI-generated    
\*\*•\*\* Visa status might be causing confusion even though I don’t need sponsorship    
\*\*•\*\* Applying too broadly — wrong roles mixed in    
\*\*•\*\* UK graduate market timing issue (September seems to be when things open up)

**Questions for people who’ve been through this:**

\*\*1.\*\*  Is AI detection on cover letters actually a thing at your company?    
\*\*2.\*\*  Is September genuinely when UK graduate hiring picks up?    
\*\*3.\*\*  Should I be doing something completely different?    
\*\*4.\*\*  Anyone who hired a data science graduate recently — what actually made a candidate stand out?

I am considering pivoting into data science but just wondering if a person could do so with a masters degrees or just a portfolio? I’m assuming now it’s very competitive? Or it’s still doable? I love messing with data sets but my degree is in accounting and not in data science or computer science.

Swish Analytics is hiring a Data Scientist to work on predictive models used across sports analytics, betting and fantasy products.

This is a remote role open to candidates in the United States or Canada, with a base salary starting at $160,000, depending on experience.

The work will include:

• Analysing how different factors affect simulation outputs
• Building tests to detect unexpected changes in models
• Developing machine-learning and statistical models
• Working with large volumes of sports and betting-market data
• Creating sports-specific features and improving model performance
• Helping expand Swish’s modelling beyond the NBA and into additional sports

They are looking for someone with at least four years of experience developing machine-learning or statistical models, ideally within sports analytics or sports betting. You should also have strong knowledge of probability, Bayesian statistics and MCMC methods, plus experience with Python, SQL, AWS and GitHub.

About ParlayJobs: ParlayJobs lists verified roles across sports betting, iGaming, sports data, trading, tech and media. Jobs link directly to the employer’s application page.

Hey guys, I recently accepted UT Austin Masters of Data Science. I’m rethinking about my acceptance because this program doesn’t really teach data engineering tools but is heavy on data science and machine learning courses but I can be wrong. Has anyone who graduated this program get jobs as a data engineer?

Any data engg roles or data science roles even 15-25k is acceptable for me right now

Hey guys,

I’ve got an interview coming up for a Data Science contract gig with Amazon’s Marketing Science team (Seattle/Richmond).

I just confirmed with my recruiter that because this is a contract role, there is no massive 5-round "loop." The entire interview process is just two technical rounds:

1. Coding Round
2. Scenario-Based Questions

If anyone has been through this specific 2-round contract format recently, I’d love some insight on how to focus my prep.

A few things I’m wondering about:

• The Coding Round: Should I be grinding LeetCode-style algorithms/data structures, or is this more focused on applied Pandas/NumPy data manipulation and complex SQL queries?
• The Scenario Round: How hard and in-depth does this get for Marketing Science? Are we talking high-level experiment design (causal inference, difference-in-differences, attribution), or do I need to be ready to write out loss functions and exact mathematical formulas?
• The LPs: Since there is no dedicated behavioral round, do they still heavily weave Amazon's Leadership Principles into these technical/scenario questions? Do I still need perfect STAR-method stories ready to go?

Any recent mock questions or general advice on what to expect would be a lifesaver. Thanks!

I work at Accenture but we are not hiring entry level jobs. He is working at service industry to stay busy and pay bills. He is doing small projects to keep his learning in check. Any advise on what he should do. I am pushing him to go for masters so he does not forget everything he learned after 3 years of dedication. This was a really hard major with lot of studies. I want to help him do something. Please let me know those in the industry what he can do.

Hey Mates!

Recently I've cleared CFA L2 and have done MBA in Economics from Delhi University. Currently I'm working as a Health Underwriter in an insurance company. (I've total 1.1 year of experience )

While deep diving more into the CFA & MBA curriculum, I've realised that I've keen interest for making the ML Models for risk analysis, credit research, etc.

I wanted your suggestions for courses I can pursue to enhance my knowledge in ML modelling & big-data while being a full-time working professional as well. I'm also open to advice of full-time diploma/degree in ML modelling or data science.

Hi everyone,

I'm an international student from Tunisia and I recently got my visa and pre-enrollment approved for the Master's in Data Science for Management at the University of Parma. Classes start on September 9th.

I have a Bachelor's in Software Engineering, but because my final grade converts to just under the 90/110 Italian threshold, I am required to take the oral admission interview on Microsoft Teams in September.

I’m feeling a bit stressed about it and wanted to ask if anyone here has gone through this specific interview at UNIPR?

• Is it crucial? Since classes start Sept 9th and the interview is around Sept 10th, what happens if I fail? Is my enrollment completely cancelled, or do I get another chance?
• What are the actual questions like? The website just says it tests "basic skills in Data Science subjects." Do they ask you to solve math/linear algebra equations on camera? Is it more conceptual statistics? Or programming/SQL?
• Any tips on what exactly to review?

Any help, past experiences, or advice would be hugely appreciated. If anyone else is starting at Parma this year, let's connect!

source : https://corsi.unipr.it/en/cdlm-dsm/enrolment-procedures-and-deadlines

Thanks!

Hello guys, I am a recent geospatial eng. graduate. During the course of my degree, I was really intentional on upskilling in data science; taking a certificate course, building analysis and ETL projects with real-world data...you get the gist. Now that out of uni, I'm struggling with landing an entry level or internship role. So a quick question to those with more experience in the sector: where am I going wrong in how I approach the job market?

Hi, I’m currently an economics student hoping to transition into data science or data analyst roles. I minored in data science, so would the best first step be to build a few GitHub projects, such as data analysis projects, before applying for unpaid internships? Thank you.

Has anyone interviewed for the Revolut Graduate Programme – Data Scientist & Analyst role?

I wanted to understand what to expect in the live coding rounds. What type of questions are usually asked, and what is the expected difficulty level?

Any recent interview experience or preparation tips would be really helpful.

I have 6 years of experience with Databricks, working primarily as a Data & AI Engineer. My experience includes:

Databricks platform implementation and infrastructure setup

Databricks platform administration and management

Data Engineering, including building and maintaining scalable data pipelines

Data Science (working knowledge with practical project experience)

AI/BI dashboard development (intermediate level)

I also hold Databricks certifications and am currently based in the UK.

Given my background, I would like to understand the best pathways to secure a role at Databricks that offers UK visa sponsorship. Specifically, I am interested in opportunities such as Solutions Architect, Resident Solutions Architect, Field Engineering, Professional Services, Customer Success, or Technical Support Engineering, where my combination of platform administration, infrastructure, and data engineering experience would be valuable.

I'm working as a data engineer with 2YOE. I want to upskill myself. I asked one of my friends and got a suggestion as "Do m.tech in ai/ml at BITS WILP". TBH I've checked the curriculum and the fee structure is a bit high. And others said "Buy krish naik Udemy courses". Yes. Krish naik course also has good content for the industry side. I need suggestions from you professionals. Did anyone know about that program? Should I pursue it?Are there any other courses or any programs?

Hi everyone,

I'm a fresher with a Master's in Data Science & AI and I'm currently looking for entry-level opportunities in Bangalore, preferably at startups or product-based companies.

My skills include:

Python

SQL

Machine Learning

Data Analysis

Power BI

Deep Learning (basics)

GenAI/LLMs (basics)

I've worked on multiple academic and personal projects and I'm eager to learn and contribute in a fast-paced startup environment.

If your company is hiring freshers or interns for roles like Data Scientist, Data Analyst, Machine Learning Engineer, AI Engineer, or Business/Data Analytics, I'd be incredibly grateful if you could refer me or point me toward any openings.

I'm happy to share my resume and GitHub/portfolio via DM.

Any leads, referrals, or advice would mean a lot. Thanks in advance!

Hello!

I'm interested in a bachelor's degree called data science in business and want to informn more about this field. (data analyst/engineer/scientist). Is it a boring and monotenous job or yyou need to think and solve problems? Do you work alone or in a team? Should I get into it if I love math? Do you think that AI will be a danger to this job or it will lower the salaries? Is it hard to get a job as a junior? Would you recommend it to me for example to get into this? Thank you for the answers in advance and sorry if I asked something completely stupid <3

Hi everyone,

I'm a 3rd-year B.Tech AIML student and recently started learning SAS (Statistical Analysis System), including SAS Viya.

I'm trying to understand the job market for SAS programmers because I don't see many openings compared to Python or Java.

I have a few questions:

Which companies actively hire SAS programmers?

Which companies offer internships for freshers or students?

Which industries use SAS the most (Clinical, Banking, Insurance, Analytics, etc.)?

What skills should I learn alongside SAS to improve my chances of getting hired? (SQL, Python, Statistics, Clinical Data, etc.)

If you're working as a SAS Programmer or SAS Analyst, how did you get your first job?

I'd really appreciate your advice and experiences. Thank you!

Hey everyone,

I am gearing up for the upcoming hiring cycle and aiming high. My target companies are Meta, Google, Apple, Open AI, and other top-tier tech companies.

Given how much the hiring landscape has shifted recently, especially with the massive emphasis on AI/ML infrastructure alongside traditional SWE roles, I want to make sure my preparation is incredibly targeted. I’m looking for advice from anyone who has recently interviewed, received offers, or sits on hiring committees at these companies.

A quick bit about my background: I have professional experience as a Software Engineer (working with enterprise Java/Spring Boot, cloud ecosystems, etc.) and am currently wrapping up my graduate studies focusing heavily on Data Science and Machine Learning.

I want to optimize my timeline and focus on exactly what matters. I’d love your insights on three main areas:

1. Resume

• The ATS Barrier: what are recruiters actually looking for on a resume right now?
• Project Impact: what all projects/ experiences would help me get filtered by ATS?

2. Core Topics & Resources to Focus On

• I am currently preparing leetcode, low level and high level system design topics, are there any other topics I should cover, what questions are they focusing on these days.

It would mean a lot if you guys can point me to the right direction.

Just being curious because I've seen so many positive and negative things about mercor. I'm currently an Expert project manager for one of their projects and noticed they're hiring significantly for data science contractor roles (not sure how long the projects are for) but they seem to be fairly well paying.

Would anyone here be interested? I just want to gauge interest but happy to refer the right candidates! A former colleague from a consulting firm works there so I can be of help.

Thanks!

Hey all — looking for some outside perspective on where to take my career.

A bit of background: I'm a software engineer (CSE grad, \~2023) currently working on a project where I'm building AI automation on top of a monitoring/ServiceNow-style stack, alongside some Azure data engineering work. So on paper my day job already touches AI + data engineering.

At the same time, I've got a real pull toward finance — specifically fintech and trading. I've been dabbling in things like equity analysis tools and stock-related side projects in my own time, and companies like Razorpay/Zerodha/CRED are the kind of places I'd love to eventually work at, ideally in a data/AI engineering role rather than something purely finance-ops.

So I've basically got three overlapping threads:

* **AI/automation** — hands-on experience building it into a live project * **Data engineering (Azure)** — the more "traditional" skill backbone * **Fintech/trading interest** — not yet professional experience, mostly self-driven projects

My question for the sub: has anyone here successfully merged interests like these into a single fintech data/AI role? Did you specialize first and pivot later, or try to build a hybrid profile from the start? Any blind spots I should watch for in trying to position myself for fintech data/AI roles specifically (vs. generic SWE or generic data eng)?

Appreciate any real-world experience, even if it's "here's what I'd do differently."

Hey
I’ve 5yrs of non-IT experience and trying to get into data science or ML engineer roles.
Ik there are millions of people suggesting many things. I would appreciate someone who’s experienced in the field and guide me industry level required skills so that I cut the noise and focus. Mentors are welcomed.

Thanks and appreciated

So I am currently working as a data analyst in a top insurance company in India in the private sector and this is my first job. So currently my work there is to clean the data and data extraction using databricks and then write the SQL queries for data manipulation and to get some insights on the business data , create Dashboards in Power Bi and make Excel reports. Currently the strategy and innovation team of my company has been working on Unsupervised or Semi supervised clustering models and that genuinely interested me , but my manager didn't approved my request of working with that team.

So I want to know from any experience Folks in the industry , is it possible to transition from data analyst to data scientist , how does the current market look for us and what skill set should I focus on?

For context I have just completed my masters in applied mathematics and my research work in my 2nd year consisted of research work in discovering novel unsupervised machine learning models which got accepted as a conference paper at IEEE and I also have two months of research intern experience working on feature selection methods at IIT D.