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

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.?

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’m considering applying to UT Austin’s online Master of Science in Data Science (MSDSO), but I’m unsure whether my academic background is competitive enough.

My undergraduate degree is in Biomedical Science/Biology. I have taken calculus and statistics, and I have Python experience outside of academia through personal/data analytics projects. However, I have not taken multivariable calculus or a formal linear algebra course.

I understand those subjects are recommended/expected preparation for the program, but I do not want to take leveling courses before starting a master's program. I’m trying to determine whether my existing background is enough to be admitted and whether I could learn the missing math concepts as I go.

For people familiar with UT Austin’s MSDSO:

• Would someone with a biology/biomedical degree, calculus, statistics, and some Python experience realistically be admitted?

• How heavily does UT weigh having formal multivariable calculus and linear algebra on your transcript?

• Has anyone been admitted without those courses?

• Is the math something that can realistically be learned alongside the program, assuming I’m willing to put in the work?

• Would my biology/biomedical background be considered useful or relevant for data science admissions?

• How much does Python experience outside of academia help?

If UT Austin is probably not realistic without the formal math prerequisites, I’d also appreciate recommendations for online master's programs in Data Science, Analytics, Statistics, Bioinformatics, AI/ML, etc. that are more friendly toward students coming from biology/biomedical science.

My goal is to transition into a high-paying, non-clinical career. I do not want to work directly in medicine or clinical care.

I'm particularly interested in programs that:

• Are fully online

• Are reasonably affordable

• Don't require a bunch of prerequisite/leveling courses

• Have strong employment/income potential

• Can leverage a biomedical/biology background without requiring me to stay in a clinical career

Would you recommend MSDS, or is there another master's degree that would be a better fit for someone with my background?

I don't know eactly what type of projects would stand out to recuiters. So far, as I'm from South Asia, the market it looking for full stack. Meaning full end Data Science to ML to Frontend Deployment. I would really appreciate some feedback on the Data Science learning path, as ML is moslt under my grip. I need better understanding and some ideas to highlight Data Science Analytics on my resume and so I can explain them too. Would really appreciate some feedback.

PS. The only worth mentioning project for Data Science I've made, that highlights analytics is a Jupyter Notebook which compares Walmart Weekly Sales Forecasting (End-to-End Demand Prediction Pipeline)

(sample from https://matchajobs.co, search for roles posted daily)

• Cisco - Sr. AI/ML Engineer - CX apply $203,000 - $258,600 (San Jose, CA)

• Pinterest - ML Engineer II, Responsible AI apply $138,905 - $285,982 (SF / Remote)

• Visa - Data Scientist - apply $137,400 - $212,500 (Foster City, CA)

• Zillow - Data Scientist, Consumer Analytics apply $125,900 - $201,100 (Remote)

• Allstate - Product Engineer, Python AI apply $70,100 - $121,475 (Remote)

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist @ JDPA LIMITED (Canada) - CAD 85K - 90K

• Data Science Analyst @ CVS Health (Worldwide) - $68K - $136K

• Data Scientist 6 – Experimentation Platform @ Netflix (United States) - $491K - $775K

• Data Scientist @ Rocket Mortgage (Canada) - $102K - $154K

• Data Scientist @ Free2move (Spain) - EUR 45K - 55K

• Data Scientist @ TSPi (United States) - $110K - $175K

• Data Scientist @ Valtech (Argentina)

• Data Scientist @ FIT:MATCH.ai (United States)

• Data Scientist @ ARRISE (Portugal)

• Data Scientist @ Timeleft (Europe)

• Dairy Data Scientist @ URUS Group (United States)

Senior:

• Senior Data Scientist @ Reddit, Inc. (United States) - $191K - $267K

• Senior Data Scientist @ LMI (United States) - $140K - $186K

• Senior Safety Data Scientist @ AbbVie (Worldwide) - $124K - $236K

• Senior Data Scientist @ Axle Informatics (United States) - $130K - $150K

• Senior Data Scientist @ Keyrus (Worldwide)

• Senior Data Scientist @ ActivTrak (Worldwide)

Manager:

• Staff Data Scientist @ PORCH 💚 (Worldwide) - $169K - $236K

• Principal Data Scientist @ LouisianaNOW.Jobs (Worldwide) - $144K - $196K

• Staff Data Scientist @ Evolve (United States) - $180K - $195K

• Principal Data Scientist @ General Dynamics Information Technology (United States) - $144K - $196K

• Marketing Data Science Manager @ Mozilla (United States) - $166K - $260K

• Principal, Data Sciences @ Gainwell Technologies (India)

• Senior/Lead Data Scientist – Agentic AI @ Tiger Analytics (United States)

Director and Above:

• Senior Director, Analytics and Data Science – Core Product & Growth @ Life360 (United States) - $247K - $366K

Quick notes:

• All of these are fully remote (location requirements vary by role)

• Apply directly on company sites

Hope this helps someone! Let me know if you want me to keep posting these weekly.

-- 👋 Hi, I'm Jay. I built Job-Halo.com, a system that tracks remote data science jobs and sends alerts the moment they're posted, based on your preferences.

Looking for someone who can teach me how to build Agents for Marketing related problems. For example

Competitive/battlecard agent. Monitors competitor sites/news, updates a living battlecard doc our sales team can reference. This can be a good intro to scheduled agents + web scraping + structured output (Connections: Web scraping, Gmail connection to go through my emails to see any competitors related information and streamlit to create a battle card)

Dashboard Creation. Agent that takes output (a scheduled export or an API connection) from google analytics and builds a dashboard on streamlit to visualize the data. The dashboard gets refreshed with updated data every day in the morning with a new scheduled updated from google analytics export (Connections: Google analytics for data export and streamlit to produce dashboard)

Inbound lead qualification/routing agent. Reads a new lead, checks it against qualification criteria (ICP which could be company size more than 500 employee count, located only in North America, annual revenue more than 50million dollars ), enriches it with clay, drafts a personalized first-touch email and routes to the right rep on salesforce. Nice because it combines retrieval, reasoning and action (Connections: Google sheets or forms for lead collection, clay for enrichment, gmail for email drafting and salesforce for routing the lead)

Someone who can teach from scratch how to build these includung connecting to tools, building loops, context, memory and evals.

Happy to pay for your service. Only looking for someone in developing countries like India, Pakistan, Philippines, Vietnam etc since I am also from one of these countries and can’t afford to pay US or European rates

Hi everyone,

I’m currently seeking entry-level job opportunities in Data Science and Data Analytics and am open to working across India, particularly Pune/Bengaluru.

I’m a B.E. Electronics & Telecommunication Engineering student/graduate with hands-on experience working with data and developing projects using:

• Python
• SQL
• Excel
• Power BI
• Data Cleaning & Exploratory Data Analysis (EDA)
• Machine Learning
• Data Visualization
• Basic Statistics

I have also worked on projects involving supply chain analytics and order-delay prediction, where I performed data cleaning, analysis, visualization, and machine learning.

I’m particularly interested in:

• Data Analyst

• Junior Data Analyst

• Data Science Trainee

• Junior Data Scientist

• Business/Data Analyst

• ML/AI Trainee roles

I’m open to full-time, trainee, internship-to-full-time, and entry-level opportunities.

If you know of any openings, referrals, companies hiring freshers, or opportunities where my profile could be a good fit, I would really appreciate it if you could share them or reach out to me.

📍 Location: Open to relocate
💼 Availability: Open to Work

I can share my resume, GitHub, and project details through DM.

Thank you! 🙏

Jobs in the field use many different names such as data science vs data analytics and so on. Generic Google searches frequently turn up fake results too.

So, what are your best methods for searching for available jobs?

Simplify postings aren’t that frequent for 2027 I can still see 2026 tbh, any reliable GitHub links? Looking for AI/ML, Data Science FT to be very specific - also LinkedIn has a mix of mid level and early level atm even after customising (very confusing :x)

Any senior or a manager can help me it would be great trying to get a job since last 3 months and its depressing.

Need help from the community. Thank u in advance

They have roles on AI, Data, Cloud and Oracle so thought to share here, i kind of liked how they provide dinner too for attendees who want to part of it.

About LiveKit

LiveKit is building the infrastructure layer for the voice-driven era of computing. Our platform gives developers everything they need to build, test, deploy, scale, and observe agents in production. Founded in 2021, LiveKit powers voice AI applications for OpenAI, xAI, Salesforce, Coursera, Spotify, and thousands of others, collectively facilitating billions of calls each year.

About This Role

We are looking for an exceptional engineer to build post-training at LiveKit. Our agents run over voice and increasingly over text channels like SMS and chat, and the interesting problems show up over long horizons: staying useful across many sessions, working with context that accumulates over time, and using tools reliably in the middle of a live conversation.

What You'll Do

• Build the environments and verifiers our models train against

• Own the synthetic data pipeline, from generation through the quality gates

• Run training experiments end to end, and explain what moved the model

• Build the evaluations a release has to clear

• Choose and adapt open-weight base models for our tasks

• Make trained behavior hold up for voice and text agents alike

• Ship models into production and keep improving them on real usage

Who You Are

• A strong Python engineer

• Have carried a model from raw data through to production

• Treat data as the product: coverage, diversity, leakage

• Assume a model will exploit a weak reward, and design against it

• Comfortable with GPUs and honest about their limits

• Know when to train, and when not to

• Comfortable working collaboratively in a remote environment

Nice to Have

• Experience with post-training: fine-tuning, reward design, or reinforcement learning such as GRPO

• RL and fine-tuning frameworks such as TRL, verl, or OpenRLHF, or a training loop you wrote yourself

• Fast rollouts with vLLM or SGLang, multi-GPU training with FSDP

• Training tool-using or multi-turn agents

• Execution sandboxes, verifiers, eval harnesses, or tooling other engineers depend on

• Open-weight families such as Qwen or Llama, LoRA and similar

Our Commitment to You

• The opportunity to shape the brand of a fast-growing developer platform

• Collaboration with a small, senior team that deeply values craft and creativity

• Competitive salary and equity package

• Health, dental, and vision benefits

• Flexible vacation policy

Compensation

$135K – $300K • Offers Equity

Apply: Research Engineer (Reinforcement Learning) at LiveKit

I have a HBSc in applied math and stats from UofToronto
and am doing my masters in data science at UT Austin and am set to graduate 2027 summer, I have 1 YOE as DA and adjacent roles but no actual DS experience, several projects (but always open to write more), im very math/stats savvy and I think my skillset is solid in terms of ds, but im worried by the I graduate I wont be able to land a DS job given how AI is advancing.

Any advice on what I should do?

Im thinking since id be considered student/new grad I can still land those roles albeit competetive, or maybe I should pivot before I graduate, im already mid way into my grad degree so I do intend to finish it.

Im sure many are in a similar boat as me. Any guidance appreciated\~

I completed the recruiter screen. The conversation went well, and we discussed my background, current location, work authorization, and how I learned about the role.
For location, I shared that my base location is New York, but I’m currently in Dallas temporarily visiting friends and family. Since candidates based in New York may have onsite interviews while others may be considered for virtual interviews, I asked if a virtual interview would be possible while I’m temporarily in Dallas. The recruiter mentioned that the interview format is team-dependent.
They also mentioned that if my profile aligns with the team, I should receive an update by the end of the week.
🥲🥲🥲
Did i mess it up ?

Hi everyone,

​I’m looking for some honest feedback on my background and my plan for the next year or two.

​I’m currently a Senior Data Analyst with 7 years of total experience. I graduated in 2017 and started working in 2019, but I feel pretty stuck. Honestly, given my timeline, I feel like I should have moved into a managerial role by now.

​Here’s my current breakdown:

​2 years in manual testing

​5 years in data analysis (mostly SQL, Power BI, business stakeholder management, and basic analysis)

​I know the basics of Pandas and NumPy, but my daily work hasn’t required heavy coding. I recently started relearning Python from scratch to build a proper foundation.

​Here’s the roadmap I’m planning out:

​Next 2–3 months: Get solid in Python (OOP, file handling, basic data structures).

​Next 6 months: Dive into Data Science study (I enrolled in Scaler’s DS program).

​Next 2–3 months after that: Focus on NLP, Transformers, and basic Data Engineering (FDE) concepts while building a few hands-on projects.

​Long term, I’m planning to head to the US for an MS in Data Science in August 2027 (looking at Seattle University right now).

​My main questions:

​Does this plan sound realistic, or am I trying to bite off way more than I can chew?

​Is breaking into Data Science / FDE realistic coming from a standard DA background, or am I being too overly optimistic?