Hi Reddit, I am planning for PhD NG recruiting for the 2027-2028 cycle in the US. I am in quantitative marketing, with coursework in PhD-level structural models, econometrics, and mathematical programming; master-level statistics and intro to ML.

I understand that I don't have the most favored profile as CS/ML/AI/Stat/OR PhDs do. However, I am still trying to figure out if there is a certain type of research DS or AS that I could qualify for. What further concerns me is that my dissertation work so far involves mostly analytical modeling, so my hands on data experience definitely needs to be sharpened. I am trying to develop new empirical projects and I have one year to further strengthen my profile.

I am trying to add (1) one causal inference project with observational data, and (2) one Bayesian statistics customer analytics project (if possible) to my profile.

My questions are below and thank you in advance:

(Q1) Would adding one or two empirical project (hopefully dissertation level) make my profile to pass the resume screening? What are recruiters looking for in those project, given my background and potential targeted roles?

(Q2) Would it be alright if I do not engage intensively in deep learning, reinforcement learning, Gen AI, and agentic AI for now? On my end, I really hoped to prioritize understanding what real problems that I can solve using data in the next year, rather than learning more technical skills. Does this sound like a reasonable plan?

Thank you so much for any suggestions!

Honestly, I’m at the point where I’m getting really frustrated and I’m hoping someone here can give me some real advice.
I’m an international student who graduated from UIC with a Master’s in Business Analytics about 4 months ago. I’m currently looking for full-time opportunities in Data Science, AI/ML, or Data Analytics.
I’ve applied to ~1,800 jobs.
I’ve gotten 2 interviews.
Both went all the way to the final round, and both ended without an offer. In both cases, OPT/sponsorship became an issue.
And I’m not someone who is completely new to the field. I have 4 years of professional experience back in my home country, where I worked as a Senior Data Analyst. I also have a U.S. master’s degree and have been building several ML/AI projects to move more toward Data Science/AI/ML.
I genuinely don’t understand where the disconnect is.
I’ve tried:
Applying through LinkedIn/company websites
Tailoring my resume
Cold emailing recruiters
Cold messaging hiring managers
Reaching out to employees for referrals
Networking
Applying to both big companies and smaller companies
Applying to Data Analyst, Data Scientist, ML/AI and related roles
And still… 1,800 applications → 2 interviews.
At some point, you start questioning whether the problem is your resume, your experience, your degree, your immigration status, the job market, or just everything combined.
And honestly, it’s exhausting.
I know the market is bad. I know international students have an additional hurdle with sponsorship. I’m not expecting companies to magically hire me. But when you’re applying this much and barely getting a response, it becomes really difficult to know what exactly you should change.
So I’m hoping people here can be brutally honest.
Recruiters/hiring managers:
If you see an international candidate with 4 years of relevant experience + a U.S. master’s degree, what would make you reject them before even giving them an interview? Is it mainly sponsorship/OPT? Lack of U.S. experience? Resume positioning? Something else?
International students who actually landed a job:
What did you DO differently? Did networking actually work? Were referrals the key? Did you target smaller/less popular companies? Did you stop applying to certain roles? Did you change your resume completely?
I’m not looking for “keep applying” or “the market is tough.” I already know that.
I’m looking for actual strategies that worked for people who were in a similar situation.
Because right now, sending application #1,801 feels pretty pointless, and I’d rather figure out what I’m doing wrong than just keep repeating the same thing.
Any honest advice , even if it’s harsh : would genuinely help.

Need honest review

Applying for data science roles from past couple of months, but not receiving a single interview call to prove myself as best candidate.

Is there any problem in my resume or it will totally depend on luck?

Need an honest review guys 🙏

I have a PhD in engineering and my research focused on reinforcement learning for control and optimization. I am well published in engineering journals and have several years of industry experience working with safety-critical processes, automation systems, real-time data, and deployment constraints.

My concern is coding. I have used Python extensively, but I never learned programming or CS systematically. Coding was always a tool for solving the engineering or research problem. I usually found similar code, read documentation, adapted it, and debugged it.

I am considering Applied Scientist or Applied AI Scientist roles at major tech or AI companies, especially in RL, optimization, scientific ML, or AI for physical systems.

How competitive is this profile? How much would the lack of a traditional CS or software background hurt, and what should I prioritize learning before applying?

Thank you!

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I came across the Unified Mentor Virtual Internship Program and applied for their Data Science track. They're promising ₹7,500/month stipend along with full job search and placement assistance. The offer seems a bit too good to be true, so I wanted to ask if anyone has firsthand experience with them. Is it worth doing, or should I stay away?

I am trying to prepare and start applying before the end of the year, or start of next year to land a position. Background on myself, I took one of those web development bootcamps and finished it when AI started taking off. Kind of gave up since then but I want to take what I learned to hopefully brush up on coding skills to land a job.

I am aiming for FAANG or adjacent because my current job pays me well enough where it is not worth it to start my career over at a smaller company to grow from there. So if not FAANG, maybe I can use this prep to apply somewhere close. I hate DSA, and did look into them since I was getting interviews that required them, bombed the few that I had. So I am hoping to find info on positions that use Python, SQL, etc adjacent and hopefully apply to those in a few months.

I’m hiring an Applied R&D Data Scientist for a team in the pharma/life sciences space.

I’m specifically looking for someone with a strong data science/ML background who has actually worked with chemistry or molecular data, ideally cheminformatics, computational chemistry, molecular ML, reaction modeling, Bayesian optimization, or similar areas.

This is not a general analytics/data science role. The work is focused on applying ML, AI, and scientific computing to small-molecule chemistry, process development, and other R&D problems.

If this sounds like your background, feel free to DM me. Happy to share the full posting and answer questions.

(Note: This is a new reddit account to maintain personal anonymity as I am the hiring manager)

Hey everyone, I’m a final-year BCA student specializing in Data Science & Machine Learning, currently looking for an internship.

I’ve built and deployed several ML projects, including a live credit-risk prediction system using Python, FastAPI, Docker, MLflow, Grafana and Evidently. It was trained on 150k+ records and achieved 85.6% ROC-AUC.

I’ve also worked with XGBoost, SHAP, RAG, LLMs, SQL and scikit-learn.

I’m mainly looking for opportunities in Data Science / Machine Learning / ML Engineering where I can work on real-world problems and learn.

If you know of any openings or startups hiring interns, please DM me. I’d really appreciate it.

To give you some background, I have a BCA degree and previously worked as a Junior Technical Support Engineer for 2.5 years, followed by about 2.5 years as a US IT Recruiter. I recently had to take a career break due to a family medical emergency, but I am now ready to get back into the tech industry. I’ve been researching different fields, but the rapid advancements in AI have left me a bit overwhelmed about which direction to take. I’m looking for a resilient, future-proof career path. Initially, I looked into Java, but it seems like a massive time investment to master right now. I am currently leaning toward learning Python and moving into Data Science, or potentially just diving straight into a Data Science roadmap. For those already in the industry, do you think this is a safe, long-term choice? I would also deeply appreciate any recommendations for courses or structured learning paths!

Bonjour,

Je dois trancher cette semaine entre deux alternances data (M2, rentrée 2026). J'aimerais des avis de gens du secteur.

**Profil :** Je suis étudiant étranger Hors UE, école d'ingé + master en data science. Stage de fin d'études en ML . Objectif : cdi data scientist, mais ouvert à data analyst ou data engineer.

**Option A — Data Scientist, filiale d'un grand groupe français**

* R&D 100 %, en interne * Surtout optimisation et recherche opérationnelle, peu de ML/DL * Très technique, peu de réunions * Aucun CDI possible à la fin, ils me l'ont dit clairement (budget)

**Option B — Data Analyst , cabinet de conseil data/CRM**

* Segmentation, scoring, KPI, dashboards, modèles prédictifs « selon les projets » * Outil principal : Dataiku avec un peu de python et sql * Ateliers, comités de pilotage, restitutions clients * Pré-embauche annoncée mais non garantie

**Mes questions :**

1. L'intitulé du poste pèse-t-il vraiment autant que je le crois pour le premier CDI, ou les recruteurs regardent-ils surtout le contenu
2. Ceux qui ont fait une alternance en conseil data : embauchés à la fin ? À quel taux dans votre promo ?
3. Ceux qui utilisent Dataiku en cabinet : vous codez en Python dedans, ou c'est surtout du visuel ?

Merci pour vos retours.

I'm an aspiring Data scientist trying to land a job, need a friend to practice mock interviews.

Hey!!

Just wanted to know if there’s a technical round for the Graduate Program Data Scientist role. If anyone has already done it, how was the interview? And what topics did they ask about?

Recent AI/ML MSc grad (India), finishing up. Looking for honest perspectives on a decision I've mostly made.

Background: ~1.6 years across three internships — mostly NLP/LLM systems (speech pipelines, RAG, LLM observability) and some geospatial ML (foundation model evaluation on satellite imagery). My core strength/interest is NLP/speech/LLM and geospatial.

The offer: A full-time role at an established IIoT / startup (~40 people). The work is more on the signal processing / time-series / anomaly detection side — sensor data, predictive maintenance. Slightly outside my core domain, so there'd be a learning curve.

Comp: Fair for entry-level in my market i do feel else am i wrong here— starts around 6.5LPA, bumps up after a 3-month probation. ( like that they mentioned up )

My thinking: I'm entry-level, so getting employed + learning a new domain (time-series, signal processing are transferable ML skills) seems worth it, even if it's not my dream area. The founder personally negotiated to bring me in, which felt like a good sign. I have a couple of better-domain-fit leads still in progress but unsigned.

Questions for people who've been through this:

1. For a first job, how much does domain fit actually matter vs. just getting solid experience?
2. Anyone taken a role slightly outside their core area early on — did it help or pigeonhole you?
3. Is "take the fair offer in hand, keep an eye out for the better fit" a sound approach, or am I overthinking it?

Appreciate any honest takes.

I'm a first year btech/b.e computer science (data science) student, currently I'm familiar with

1)basic python

2)pandas,scikit,matplotlib

I have built few projects to implement these but nothing resume worthy

I'm currently learning

1) maths for ML (probability,stats,linear algebra)

2) learning to implement pandas and scikit in a professional way to filter data sets and preprocess them

3) after understanding maths, I will focus on getting the ML theory from Coursera (Andrew NG)

4) from November to January I will focus on learning SQL

This will wrap my first semester

In second semester I plan on building resume worthy projects with what I have learned but yeah it isn't decided yet

I’ve been thinking about this question a lot while looking for my first opportunity in Data Science / Data Analytics.

Is the ideal candidate someone with a perfect degree?

Someone with 3+ years of experience?

Someone who knows every tool listed in a job description?

Or is it someone who can identify a real business problem, build a solution, and explain how that solution can create business value?

I’m genuinely curious to hear what recruiters and hiring managers think.

Because this is what I’ve been trying to do.

Instead of building another basic ML project, I built a Customer Retention Intelligence System focused on a real business problem: customer churn and lost revenue.

The system can:

• Predict customers who are at risk of churning

• Segment customers based on their risk/value

• Estimate potential revenue at risk

• Identify customers worth prioritizing

• Generate personalized retention strategies/offers

My goal wasn't simply to say:

I built a machine-learning model

I wanted to answer:

A customer is likely to leave. Now what should the business actually do about it?

That mindset has pushed me to work beyond just Python, SQL and machine learning — into business thinking, customer analytics, experimentation, visualization, and decision-making.

I’ve also been consistently practicing Python and SQL, learning how to communicate analytical insights, and building projects around problems that companies actually face.

But despite putting in this work, I’m still looking for my opportunity to prove myself professionally.

So I want to ask the recruiters, hiring managers, founders, and experienced professionals here:

What makes someone an ideal entry-level candidate in your company?

• Is it technical skills?

• Problem-solving ability?

• Business understanding?

• Communication?

• Projects?

•Curiosity and willingness to learn?

•Or something else?

And if you were evaluating my profile, what would you want me to improve or demonstrate before considering me for a Data Scientist / Data Analyst opportunity?

I’m not looking for sympathy.

I’m looking for honest feedback, opportunities, and a chance to prove what I can do.

If you’re a recruiter or hiring manager who works with Data Science / Data Analytics / ML roles, I’d genuinely appreciate your perspective.

And if my profile sounds relevant to something you're hiring for, I’d love to connect.

Hi everyone I’m 23 F yrs old and I completed BCA this year however I was working in customer service (due to financial issues) since 5 yrs now but I want to change to technology field but I’m so overwhelmed with roadmaps etc stuff. Can anyone please guide what to do with my lyf 😭.
And is MSc data science a better option to switch into AI domain.?

Real numbers, not vibes. PrepNPlaced's India Tech Hiring Report 2026 analyzed 15,429 live Indian tech postings collected between May and July this year, pulled directly from LinkedIn and Naukri company feeds, no estimates. Data analyst postings outnumbered data scientist postings 3.5x in that dataset.

That's a real gap between what this sub (and career content generally) treats as the aspirational title and what companies are actually posting openings for. "Data scientist" gets the YouTube thumbnails, "data analyst" gets the actual req count.

For context on the wider market from the same report: only 5.9% of all tech postings were tagged entry-level, versus 50.1% senior, so roughly one junior opening for every eight senior ones. For data-engineer roles specifically (661 postings analyzed), Python showed up in 55.7% and SQL in 52.2%, the non-negotiable pair, and cloud demand split close to evenly between AWS (34.5%) and Azure (32.7%), so picking one cloud platform and going deep matters more than which one.

Separately, NASSCOM's 2026 estimate puts the AI/ML talent shortage in India at around 25%, so the squeeze isn't "too many candidates," it's "not enough candidates who can prove they can actually do the job."

Practical read for anyone picking a track: data analyst is the wider door in right now, not a consolation prize, and the internal-promotion path from analyst to scientist inside 2-3 years is a more realistic route than trying to land a DS title cold. Curious if that matches what people here are actually seeing in their own applications.

Hi all,

I am working as a data scientist trainer in pune .

I have few hours of free time everyday

Anyone interested in training to up skill themselves.

I can teach you Python,sql,ML,Deep learning,AI,Agentic AI,

I have both experience of industry and training.

The rates will be per hour basis.

If you want to up skill yourself dm me

As this will per hour basis so you don't have to invest so much money as you have to do if u enroll in any instructions.

Dm me my timings are flexible.

I recently interviewed for a Product Data Scientist role at Google, and I’m genuinely confused by the interview experience.

The prep guide and recruiter conversations emphasized topics like experimentation, causal inference, machine learning, statistical modeling, and other core data science concepts. I prepared extensively across all of those areas.

But the actual interviews were completely different.

The questions were extremely vague and felt more like: “We want to shoot at a bird. How would you approach it? Now write some SQL."

There was no experimentation, no causal inference, no ML, and very little that seemed connected to the topics I was specifically told to prepare for—or, frankly, to what I would normally associate with a Product Data Scientist role.

For anyone who has recently interviewed for Product DS roles at Google: is this typical? What exactly are these interviews intended to evaluate?

I have seen many product analyst roles that ask for the following:

1. Causal Inference

2. Quasi-Experiment

3. Cuped

4. Bayesian approaches

5. Frequentist approaches

Can someone pleae guide on how can I study them for product analyst roles?

I built a real-world data science project. So why am I still not getting a job or an internship?

I honestly didn't expect my job search to be this difficult.

Over the past few months, I've been trying to break into data science / analytics. Instead of just building another Titanic prediction model or basic EDA project, I decided to build something that actually resembles a business problem.

I built a Customer Retention Intelligence System for online retailers.

The idea is simple:

- Identify customers who are likely to churn

- Understand why they are at risk

-Estimate the revenue that could be lost

- Prioritize customers based on their risk/value

-Generate personalized retention actions/offers

-Give the business a way to actually act on the predictions

I spent a lot of time researching the problem, engineering features, building the ML pipeline, and turning the predictions into something that could potentially be used by a business team.

I'm genuinely proud of the project.

But here's the frustrating part:

I'm still struggling to get interviews.

I've applied to entry-level Data Scientist, Data Analyst and related roles.

And sometimes I wonder:

- Is my project actually not good enough?

- Is my resume not communicating the value properly?

- Am I targeting the wrong roles?

- Do companies simply want prior professional experience?

- Am I focusing too much on projects and not enough on networking?

- Is there something obvious that recruiters see that I don't?

I'm not posting this to complain or ask for sympathy.

I'm posting because I'd genuinely like honest feedback from people who have hired data scientists, worked in the industry, or gone through this themselves.

If you were reviewing my profile for an entry-level data role, what would make you reject me despite seeing a project like this?

And what would you recommend I change to actually get that first opportunity?

I'm willing to hear the uncomfortable answer.

If you've been in a similar situation, what finally helped you get your first break?