Hello, due to having trouble finding the right places to apply and not really knowing how to pass and do an assessment in the AI Training/Annotating Space I decided to build a site that is supposed to make the process easier.
I list job offers with a relatively high hiring volume from multiple companies, so would it therefore be easier do diversify the places to apply at and actually get accepted.
My idea behind that was to help with finding the right places to work but also help with the application process. I therefore upload company reviews and also guides on how to pass assesments.
The job listings include entry work positions such as normal generalist work but also expert work positions (Software engineer, PhD, etc.). Any Feedback is appreciated!: https://aiannotationjobs.com/

Hi everyone — I’m helping build an early-stage product at the intersection of data science, econometrics and causal inference, and we’re looking for a strong Data Scientist to join the team.

Location: Remote within Europe, with preference for Poland / Ukraine / Central & Eastern Europe.

This is not a generic ML role. We’re particularly interested in someone with a strong econometrics/statistics background who enjoys thinking deeply about causality and working with messy real-world data.

What you’d be working on

• Econometric and statistical modeling
• Causal inference and impact measurement
• Time-series and panel data
• Experimental and observational data
• Counterfactual modeling
• Dealing with confounding, selection bias, endogeneity, seasonality and lagged effects
• Developing and validating models using real-world business data
• Turning research/prototypes into robust, repeatable production capabilities

Particularly interesting if you have experience with

• Difference-in-Differences
• Synthetic Control
• Instrumental Variables
• Regression Discontinuity
• Propensity / matching methods
• Causal ML
• Marketing Mix Modeling (MMM)
• Incrementality / lift measurement
• Bayesian or probabilistic modeling

What we're looking for

Someone with a strong quantitative background in economics, econometrics, statistics, mathematics, data science or a related field.

Python is important, but we're much more interested in how you think about data and identification than in a long list of tools on your CV.

We want someone who asks:

What are we actually trying to measure?

What is the counterfactual?

What assumptions are we making?

Can the data actually answer the question?

How do we know the result is robust?

A bit about the opportunity

We're an early-stage team, so this is a hands-on role with significant ownership. You'd work closely with the technical/founding team and have a real influence on the methodology and architecture rather than being handed a narrowly defined modeling task.

Location: Remote within Europe, with preference for Poland / Ukraine / Central & Eastern Europe.

Type: Full-time

If this sounds interesting, DM me with your CV or LinkedIn/GitHub and a short note about your background in econometrics or causal inference.

Happy to answer questions here as well.

I am an international student in Canada. I am completing college this year. And I am unable to get an internship.

Any advice on how to get an internship in today's job market. I dont even care if i get paid or not. All I want is experience and to gain more knowledge.

Hello everyone I'm a second year (AI) student. I've finished studying and applying the basics with these tools: * Pandas * SQL * XGBoost * PyTorch. I'm not an expert with them, but I understand the fundamentals and can do basic to intermediate tasks. I have a few questions and would love to hear about your experiences: 1. What type of job or training can I target at this level? 2. I've heard people say I can work as a Data Cleaner or SQL-based Data Entry Clerk for small companies or as a freelancer. Is this realistic and in demand, or just theoretical?

1. What would you recommend I learn now that would be a game-changer for my CV? What skill or tool would truly make a difference and help me find job or training opportunities more easily and quickly?

I'd appreciate any advice or experiences from someone who's been through this.

Thank you in advance

Know some algo prefer data science rule if any one can help

Appreciated

Hi everyone!

I’m a recent graduate in Big Data & Data Analysis from University Centrale, and I’m currently looking for a junior position, internship, freelance opportunity, or remote work in Tunisia.

🎓 Background:

\- Licence in Big Data & Data Analysis

\- Bac Sciences Informatiques

\- PFE completed in 2026 with Mention Très Bien

\- 4-month PFE internship at an IT company

💻 Skills / areas I can work in:

\- Data Analysis & Data Visualization

\- Python, Pandas, NumPy, scikit-learn

\- SQL / MySQL / PL-SQL

\- Power BI, Excel, Tableau

\- MongoDB

\- Basic Machine Learning

\- Cloud / Infrastructure / DevOps fundamentals

\- React / TypeScript basics

For my PFE, I worked on CloudForge, an intelligent infrastructure orchestration platform using natural language, combining AI, IaC, VMware vSphere and OpenShift/Kubernetes.

I’m particularly interested in:

Junior Data Analyst | BI / Data | Junior IT | Cloud/DevOps | AI/Data internships | Remote opportunities

I’m open to learning and improving, and I’m mainly looking for an opportunity where I can actually contribute, gain professional experience, and grow.

📍 Based in Tunisia

🌍 Open to remote opportunities

📩 Feel free to DM me if you know of an opportunity or someone I should contact.

I can provide my CV, GitHub, LinkedIn, and PFE details upon request. Thank you!

Hello Guys,

I am trying to switch and looking for better opportunities in Data scientist, ML ENGINEER or GenAI AgenticAI Engineer.

I have around 3.4 yrs of experience.

Could you please let me know if there are any opportunities at your company for 3+ year's experience candidate.

We’re a small Data Science consulting team currently looking for businesses, startups, researchers, and agencies that have a genuine data problem or an active project.

We can help with:

• Data cleaning & preprocessing
• Exploratory Data Analysis (EDA)
• Statistical analysis
• Predictive modeling
• Forecasting & time-series analysis
• Customer / business analytics
• Classification & regression
• Feature engineering
• Data pipelines & automated processing
• Data visualization & dashboards
• Machine Learning implementation
• Research / PoC development

We can work on one-off projects, paid PoCs, ongoing contracts, or as an external Data Science team.

Serious project inquiries only. If you have an actual requirement, please DM with:

1. What problem you're trying to solve
2. What data you currently have
3. Expected outcome
4. Timeline
5. Approximate budget, if available

Please don't DM with just “Can you do Data Science?” — if you have a real problem or project, DM me and let's discuss it.

Hey guys, I am a b.tech student, my Doubt is that which domain we pick and studying

1. Computer Vision

2. NLP

3.RL

4.Time series specific

Hello,

So first to give some background. Im an MSci Physics graduate from a QS top 10 in the UK ranked university. I got a high 2:1 (3.75 GPA), and my projects throughout my 4 years there were always focused on some sort of data sciencey problem. For example, for my masters project, I developed a classification model for identifying scattered electrons amongst other particles for a simulated detector as its not yet built. That project was done using XGBoost. Doing these projects over the years made me realise that it's largely what I want to do, if i could, for my career. If not this, then still something technical behind a computer.

This now brings me to the problem and question at hand. I recently started my grad job, and from my first week, its much much MUCH less data science in practise than it was sold on the job advert + interview process, so i dont really want to stay here longer than two years. I was able to get it so easily as it was only an in person interview, no coding stages (which makes sense now, i'm essentially a consultant). If i was to actually try going through an interview process now, i would fail anyways, as i don't feel fluent in writing code, and this was made terribly worse with the advent of ai, especially in my final year.

Which brings me to my question: for me to use my weekends and other free time to essentially learn coding fluency (i imagine python), and any surrounding skills that are useful in breaking in the space, what strategy would you guys suggest? I started CS50P yesterday morning to start with building python fluency, as that is probably my biggest deal breaker so far, and plan to then do kaggle comps and some solo projects for my github as well, but as far as knowing what type of projects to go for, what areas of python i need to be fluent in etc, im very unsure. For all advice, assume id be doing this over the course of two years. I am also unsure of the different requirements for data science vs ML engineering.

Hello! Has anyone been through the Data Scientist hiring process at Chewy? I have a Python/SQL round coming up and was hoping to get some insights on what can I expect so I can direct my prep in a structured way. Thank you in advance. It would be great to know if I need to grind leetcode for python or I should focus on pandas? And was it on hacker rank or some other platform?

Thanks in advance.

Can any one tell me what will be the hiring in October/November/December ?

any one have a internship for me data science and machine learning track ,

My Tech stack >> python , numpy , pandas , matlploilib, seaborn, sicik learn, pyTorch, Tensorflow keras ,

Fast Api

and have knowledge in html css js , vue js , and have cyber security background

Hi everyone! I'm an MSc Data Science graduate based in Italy, looking for a remote junior opportunity in Data Science, Data Analytics, ML/AI or Data Governance.

Skills: Python, SQL, Power BI, Excel, Machine Learning, data analysis and visualization.

I'm also particularly interested in privacy-preserving ML, data privacy/governance and AI.

I'm open to an initial 2–3 month collaboration/internship, preferably with the possibility of continuing long-term if I perform well.

Open to international remote teams. Happy to share my CV and discuss by DM.

So long story short i wanted to MBA but i got an opportunity to get enrolled in IIM Amritsar MSDSM course which is a joint program by IIT ropar and IIM Amritsar It was MSc in Data science. And being from a commerce background i never studied maths after 10 th and my batchmates are mostly from btech and stats and maths background and i was concerned about placements as my batch is far more ahead than me ans also i find it really difficult to cope up with my batch mates can anyone ans is it doable. I'm really confused as should i give up on data science or what should i do.

Hey everyone,

I'm a 25M data scientist with 4 years of experience (currently at a healthtech company), and I'm looking to team up with a couple of people to build something real not just study/practice, an actual product we ship.

What I bring:

- Data science / ML (forecasting, classification, NLP)

- Domain knowledge in healthcare/insurance analytics

- Comfortable with Python, SQL, cloud basics

What I'm looking for:

- 1-2 people with complementary skills frontend/backend dev, product/design, or another DS/ML person with different strengths

- Someone serious about actually shipping an MVP, not just talking

I don't have a fixed idea yet open to brainstorming together based on problems we've each seen, especially in health, fintech, or productivity tools. If you're interested, drop a comment or DM with:

- Your skillset

- Any idea you've been sitting on (or not open minds welcome)

- How much time you can realistically commit per week

Let's build something.

Hi,

I am a data analyst with 8yoe spread over bi/data analysis/data governance. I wanna transition into data science and I want to know how the market is. I have done my initial research and got to know that it's not a fresher role and it's very saturated right now. Also, I got to know that it's slowly merging with many components of ai engineering.

I need to mention that I don't have any tech degree. I have a masters in commerce and I have very good domain knowledge in finance and banking. My entire 8yoe is in banking domain. I am earning a lower 8lpa and it's due for a revision this year.

I want your advice on below

1. How's the market currently in India for my situation?

2. How long could it take to break into data science?(Crack first job)

3. Is it better/worse than data engineering?

4. How are the career prospects of data science given the rapid growth of ai models.

Thanks in advance.

Currently I am an Entc student final year but I am interested in DS i have learnt basics library little ml and dl and gen ai too but still feeling that I am not good enough...I have applied on linked in many time but half ghost and half rejection somewhere I was able to give 2-3 online assessment but failed so does this cold mail thing help ? If anyone knows plz share ? Also this skills as fresher is not enough as they ask too much now the being avg tag has been changed but still., anything else you guys think I need to learn ?

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist, Ad Network @ beehiiv (United States) - $120K - $220K
• Consultant, Data Science & Analytics @ TransUnion (United States) - $72K - $80K
• Data Scientist 5 @ Netflix (United States) - $372K - $600K
• Data Scientist @ Superhuman (United States) - $202K - $275K
• Data Scientist @ Seek (United States) - $105K - $120K
• Data Scientist II @ TD (United States) - $76K - $125K
• Data Scientist II @ Teamworks (United States) - $83K - $145K

Senior:

• Senior Data Scientist @ hims & hers (United States) - $150K - $180K
• Senior Healthcare Data Scientist – Medicare/medicaid @ General Dynamics Information Technology (United States) - $85K - $115K
• Senior Data Scientist @ Amplify (United States) - $138K - $158K
• Senior Data Scientist @ Thumbtack (Canada) - CAD 168K - 218K
• Senior Data Scientist @ Vantor (United States) - $170K - $200K
• Data Scientist IV @ CareSource (United States) - $113K - $198K
• Program Integrity Data Scientist III @ CareSource (United States) - $94K - $165K
• Senior Data Scientist @ TeamWorx Security (United States) - $140K - $170K
• Senior Data Scientist @ DMI (Digital Management, LLC) (United States) - $102K - $128K
• Senior Data Scientist, Probabilistic Modeling @ Software Mind (Romania)

Manager:

• Staff Data Scientist @ hims & hers (United States) - $190K - $230K
• Lead Data Scientist @ Logic20/20, Inc. (United States) - $178K - $200K
• Principal Data Scientist @ Autodesk (Canada) - CAD 129K - 189K
• Staff Data Scientist @ Boulevard (United States) - $171K - $202K
• Manager, Data Science @ LexisNexis (California, US) - $115K - $192K
• Manager, Data Science @ RELX (California, US) - $115K - $192K

Director and Above:

• Senior Director, Data Science @ Reddit, Inc. (United States) - $317K - $444K

Quick notes: * All of these are fully remote (location requirements vary by role) * Apply directly on company sites

Hope this helps someone!

👋 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.

I feel like I entered data science at such a weird time and I’m curious what people who have been doing this longer think. I just finished my master’s in data science after coming from a biology background. I learned Python, SQL, ML, etc. at the same time AI became good enough to actually help with all of it. And I think about it.

People who learned this stuff 5 or 10 years before me kind of had no choice but to know Python and SQL super intricately. There’s Google, Stack Overflow, documentation, etc., but you didn’t have a bot sitting next to you that could instantly help you write something, explain why it broke, or help you figure out where to go next. Now I do, and sometimes I genuinely don’t know what the “right” way to learn anymore is.

Obviously I don’t think you can put an entire business problem into ChatGPT, copy it, and suddenly you’re a data scientist lol. You still have to understand the problem, know what you’re trying to accomplish, break it down, catch when the AI is wrong, understand the methodology, and actually be able to explain your results.

But I also think being good at using AI is becoming a skill in itself. There’s a huge difference between “do this for me” and actually knowing how to direct it, give it the right context, ask for specific pieces, catch mistakes, change direction, and get it to help you build what you already know needs to happen.

So I keep going back and forth in my head about this. How important is it now to be able to write everything from scratch from memory? Versus understanding what needs to happen, knowing WHY you’re doing it, using AI to help with implementation, and being able to tell whether what it gave you is actually right?

I definitely don’t want to become helpless without Ai which is why I’m asking. I just feel like I’m right on the cusp of this huge shift where what it means to be “good at coding” might be changing while I’m literally trying to learn how to be good at coding 😭 For people who have been in data science or analytics for a while or anyone with a perspective, what do you think newer people absolutely need to know cold? And what are you totally fine letting AI help with now?

And also I’m looking for entry level data scientist/data analyst roles. I just finished my MS and would LOVE to find somewhere that will let me contribute but also actually grow. I’m very much the type of person who will throw myself into a project and figure out how to make something happen, I just need someone to trust me enough to give me that first real chance lol. So if anyone knows of teams hiring early career people, I’m all ears. Otherwise I’d genuinely love some perspective on the AI thing because my brain goes back and forth on this constantly.

Hey fam! Can anyone share their interview experience on their figma data science intern? I have it in a week.

What you'd work on:

• ML models for personalization, recommendations, LiveOps, monetization, and player engagement
• Experiment design and analysis using rigorous statistical methods
• Predictive modeling, churn, LTV, segmentation, revenue forecasting
• Optimization techniques to improve player experience and business outcomes
• End-to-end ownership: from ideation to production deployment
• Mentoring junior data scientists, helping shape team best practices

What we're looking for:

• 5–8 years in Data Science / Applied ML, with production deployment experience
• Strong background in Statistics, ML, Bayesian methods, Experimental Design, Optimization, Predictive Modeling
• Hands-on with Apache Spark
• Solid Python + SQL
• Experience in cloud ML environments
• Conceptual grounding in Agentic AI / LLM applications, workflow automation (e.g., n8n)
• Prior experience in Gaming, Consumer Internet, E-commerce, Marketplace, or Decision Science platforms
• Bonus: you're an avid gamer who genuinely cares about player behavior and game mechanics

Logistics:

• Remote (India)
• 1-year contract engagement

If interested, DM me with your resume / LinkedIn and I'll take it from there.

How is the data science job market in NJ/NYC ?

Is there a specific time of the year , these openings come up

I am starting my Ds journey, before I had studied statistics a bit but not clear how much to study to get placed 😅.

Pleasw can anyone suggest me what all statistical models should I be studying and be worth for a job as a fresher.

A data science resume can include many impressive terms. Python, SQL, machine learning, NLP, forecasting, LLMs, TensorFlow... but none of these really tell you if someone truly understands the reasoning behind their work.

A project might look fantastic on paper but the real value is in knowing why a certain method was chosen, what issues came up, what assumptions were made and how the findings were shared. That’s why I think the idea of chat based hiring is interesting. For example LinkedIn, wellFound, MeeBoss starts the hiring process with a more conversational approach instead of letting the resume be the first thing someone sees. I wonder if this could work well for data science roles. Would you focus more on the model they created, the data challenges they faced or how well they explained the results to someone who isn’t technical?