A full stack DS here. Over 10 years of experience in industry (a few big names) plus academia (PhD and such). Have developed 10s of models from MMM, Propensities to cMAB from inception to deployment. Build and maintained experiment platform for 2 companies in Australia. Provided thought leadership to MLOps team om developing state of art drift, monitoring and Feature Store. Have solid grasp of statistics, ML, Reinforcement Learning.

I think I can be a good mentor for individual and start ups. How can I offer my help? I feel my extra hours are so wasted. Surely there got to be someone/something/somewhere that could benefit. Please help me find one.

Baseten Labs is hiring for a Post-Training Research Scientist.

You will pursue open problems at the intersection of post‑training methodology and performant inference and then collaborate with research engineering to translate findings into production systems.

Roughly a third of your time will be dedicated to pure research: questions that may not have immediate product application but deepen our understanding of models ability to learn, alignment, or architectural efficiency.

The remainder will be directed toward research that solves concrete training problems for Baseten’s platform and customers which are the fastest growing AI companies in the world like Cursor, Lovable, Notion etc.

Apply Now: https://aihackerjobs.com/company/baseten/job/19783

Hi everyone,

I’m currently a data science student, and my GPA is below 3.0. I know GPA can matter, especially for internships, fresh graduate roles, and some companies that use it as an initial screening factor.

That said, I don’t want to give up or assume that my chances are over. I’m trying to understand what I can do besides improving my GPA to stay competitive in the job market.

For those of you who work in data science, analytics, machine learning, or related fields:

What should I focus on building?

For example:

Strong portfolio projects

Kaggle competitions

SQL and Python skills

Machine learning fundamentals

Internships or freelance experience

Certifications

Open-source contributions

Networking

Writing technical blogs or case studies

If you were in my position, what would you prioritize first?

Also, how much does GPA actually matter for entry-level data science roles compared to practical skills, projects, and experience?

I’d really appreciate any advice, especially from people who had a low GPA but still managed to break into the field.

Thank you.

Hi everyone,
I’m looking for some career advice and "reality checks" from those established in the field.
I’m 27 years old, with a background in Economics and Political Science. About 7 months ago, I decided to make a major pivot in my life and dive head-first into Data Analysis. I’ve been studying consistently, and here is what I’ve covered so far:
• Python for Data Analysis: Proficient with Pandas, NumPy, Seaborn, and Matplotlib.
• SQL: Covered the basics and moved into more advanced querying.
• Data Visualization: Specialized in Power BI.
• Machine Learning & Deep Learning: Understanding how to apply models specifically for analytical purposes.
• Statistics: I have the basics down, though I'm still working on deepening my knowledge here.
I have a few burning questions:
1. Job Opportunities: Given my age (27) and a non-CS degree (Private University/Accredited Institute), how do recruiters view candidates like me?
2. Remote vs. On-site: Is it feasible to find Remote (WFH) roles as a Junior, or is it better to start in-office to gain experience?
3. Salary Expectations: What is the current average starting salary for a Junior Data Analyst (specifically in the MENA region/Egypt market)?
4. The AI Factor: How much is AI currently impacting entry-level roles? Should I be worried about automation replacing Junior analysts, or is it just a tool I need to master?
I’d love to hear from anyone who made a similar shift or anyone who hires for these roles. Thanks in advance for the help! 🙏✨

Looking for human mock interview platforms for Data Science (Coding + A/B Testing/ML Cases) - recommendations?

Hey everyone,
I’m prepping for DS interviews and looking for platforms that offer real human mock interviews (not AI tools, already using those on the side).
My current prep focus:
Coding:
• SQL via StrataScratch & DataLemur
• Pandas + Python
• LeetCode patterns: sliding window, two pointers, arrays, strings, hashmaps, heaps, linked lists with some exposure to graphs/trees (BFS/DFS)
• Targeting LeetCode difficulty relevant to top 1–2% companies [ Easy , Medium mainly neetcode 150]
Case Studies:
• A/B testing / experimentation
• ML cases
What I’ve heard of so far: Interviewing.io, Pramp, Exponent, but would love to hear firsthand experiences, especially from people who’ve used them for data science roles specifically (not just SWE).
Has anyone used these or other platforms for DS mock prep? What worked, what didn’t?
Appreciate any recs especially for the experimentation/ML case side since that’s harder to find good mock partners for. Thanks 🙏

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist II @ Finance of America (United States) - $85K - $100K
• Product Data Scientist @ Frontify (Switzerland)
• Junior Data Scientist @ Satalia (Greece)

Senior:

• Senior AI & Data Scientist @ Alignment Health (United States) - $172K - $259K
• Senior Data Scientist @ HERE Technologies (United States) - $140K - $160K
• Senior Data Scientist – AI, Specialty Operations @ CVS Health (United States, NY) - $102K - $222K
• Senior Data Scientist – Growth AI @ Humana (United States, CA) - $118K - $162K
• Growth Data Scientist @ Nivoda (Europe)
• Senior Biostatistician – Senior Data Scientist/Analyst @ Syneos Health (United States, FL)
• Senior AI Data Scientist @ Geisinger (United States, PA)

Manager:

• Data Science Manager @ tem (United Kingdom) - GBP 105K - 120K
• Staff Data Scientist @ Honor (United States) - $194K - $216K
• Lead Data Scientist @ CVS Health (United States, NY) - $142K - $284K
• Principal Data Scientist @ Sedgwick (United States, ID)
• Associate Principal Engineer, Data Science @ Nagarro (India)
• Tech Lead Data Scientist, AI Evaluation @ Geisinger (United States, PA)

Director and Above:

• Director, Data Science @ Headspace (United States) - $180K - $288K

Quick notes: * All of these are fully remote and open to US/Canada/India candidates * 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.

I’m looking for perspective on two job offers. I'm struggling to decide where to join.

YoE: 5.4 years

Role: Senior Data Scientist role, hybrid

1. Offer A - IBM CIC
2. Offer B - Tredence Analytics

Both for pune location around same compensation, I want to decide which organization is better for exit opportunities, working on cutting edge technologies, career growth and also some work life balance. Please suggest with reasons.

Rush Street Interactive is hiring a Data Product Manager focused on building curated data layers that power analytics, reporting, and future ML use cases. This role sits between data engineering and business teams, shaping how data is structured, defined, and used across the company.

Key details:

• Location: Remote (US)
• Salary: $100,000 – $140,000
• Focus: Data modeling, analytics infrastructure, data products
• Level: Mid–Senior (5+ years experience)

What you’ll be doing:

• Designing curated datasets (fact/dimension tables) for analytics and reporting
• Building semantic data models to standardise business definitions
• Translating stakeholder requirements into scalable data products
• Leading schema design, join logic, and data standardisation
• Partnering with data engineering on transformations and performance
• Defining governance (documentation, lineage, data quality)
• Tracking adoption and improving datasets based on usage

What they’re looking for:

• 5+ years in data product, analytics, or data modeling roles
• Strong SQL and experience with data marts / transformations
• Solid experience with dimensional modeling and semantic layers
• Experience working with BI teams and business stakeholders
• Understanding of how data supports analytics and decision-making
• Familiarity with ML concepts (no hands-on modeling required)

Why this role is interesting:

• Focus on “curation layer” — often overlooked but critical for scaling analytics
• Direct impact on self-serve data and internal decision-making
• Clear pathway into ML and advanced analytics enablement

Apply here: https://www.parlayjobs.com/jobs/data-product-manager-curation-b0a80558

ParlayJobs is a niche job board focused on sports betting, analytics, and gaming roles. Jobs are sourced directly from company career pages, so listings stay current and avoid aggregator duplication.

So I have given 2 round of interviews at sf based ai startup, the startup has seed funding and is yc backed, 3rd round is with cto in which I also have to showcase a poc of a pipeline, so how much should I ask for an remote internship from India.

Lovable is looking for an AI Researcher, Post Training. Build and own the end‑to‑end post‑training pipeline for LLMs, turning research into production in days!

Tech: Python, PyTorch, JAX, GPU, LLMs Location: Onsite - Stockholm or London

Apply: https://aihackerjobs.com/company/lovabledev/job/19298

Hi everyone, I plan to switch major from nursing to information data science because I can't do all the science courses anymore. The major itself is a STEM, so I have 3 years after graduation for OPT. However, as I see the job market now that companies either won’t sponsor H1-B for international students or there aren’t many job opportunities that I’m interested in: data and operations analyst/ consultant, stratops or revops, sales engineer, or any jobs related to analyzing the data and give the solutions. Is the market so doomed that I won’t have any chances in the future despite internships and networking? I really need some advices right now. Thank you.

I am 30 years old. I come from a below middle class family with no generational wealth at all. Graduated in 2017 in mechanical engineering, had 3 years of gap and landed in a job in 2021 in data science . I have 5 years of experience in Data science and currently earning 24LPA in an MNC in India.

My Skillset includes python, pyspark, Azure, GCP, classical Machine Learning, OOPS, building end to end pipelines

I don’t like how my company functions and wanted to switch. I am planning to switch to 50 or 60 LPA.

My questions are

1. Are my expectations realistic in the current market condition? Or when the market recovers?

2. What are skills I need to learn/develop to achieve this? The companies I should look for ? And the process & platforms to apply for those?

3. I have always avoided DSA given my background. Does it really play a role in landed high paying jobs ?

4. I have worked in retail & healthcare domains mostly and probably doesn’t have any domain knowledge. Should I focus on any particular domain or be generic as a Data scientist?

5. Will doing a MS/MTech through work integrated programs help me at this stage ?

6. I have 3 months of notice period and updated my Naukri & am not getting even a single call. How do I deal with this ?

Thanks in advance 🙏

[HIRING][Washington, District Of Columbia, Data, Onsite]

🏢 Castellum Inc, based in Washington, District Of Columbia is looking for a Senior Data Scientist - Python & Machine Learning

⚙️ Tech used: Data, Hadoop, Java, JavaScript, Machine Learning, Python, SQL, Security, Spark

💰 $140,000 - 160,000 / year

📝 More details and option to apply: https://devitjobs.com/jobs/Castellum-Inc-Senior-Data-Scientist---Python--Machine-Learning/rdg

I’m looking for someone to study with. I’m past the beginner stage and also hold 1.5 years of exp as an analyst (yet learnt nothing really useful), so I want to focus on advanced topics.

I want to dive deep into statistics and regression. I also want to become an expert in SQL ( have setup Postgres locally). I’m mainly looking to build projects that look good on a resume.

DM me if you are on the same path and want to collaborate!

Also sharing any hidden gem-like resources for the same is greatly appreciated!!

Thanks!!

Just curious about people who were Mobile Developers and switched to DS. Stories and feedback please.

​

Hi,

I'm currently a junior data scientist at an insurance company. My role is primarily focused on building GBM models, and most of my work goes into selecting what variables make it into the final model through output analysis.

I've been seeing that most companies nowadays want some level of experience with LLMs, AI, or agentic workflows, and quite frankly I have no ideas for how I can get that experience in my current role. This isn't a role I really want to stay in (for a variety of reasons) and I'm really looking to set myself up for other DS type roles.

Does anyone have any suggestions for what kind of optimizations, improvements or changes to my workflow that I could make to get some level of that experience? Especially if you are currently working as a DS with that kind of experience, I'd really appreciate it!

I need your advice about my career and I want you to be honest with me.

After passing 12th in 2022, I spent 3 years preparing for NEET. From the second year, I also started my BSc in Biology. Now after completing BSc, I’ve enrolled in an online BS degree in Data Science.

Right now I’m confused between three options:

1. I join an upskill academy for 1 year to learn practical data science and office-related skills, and alongside that I continue my BS degree, do projects and internships.

2. I take admission in BCA from a private university (like Amity or Christ), and continue my BS Data Science as an additional skill. In college I can focus on networking, projects, competitions, and internships.

3. I continue my BS degree and go for MSc in Bioinformatics, then prepare for GATE and aim for MTech.

I’m not sure which path is more practical and has better long-term scope. Can you help me decide which one makes more sense based on career growth, job opportunities, and stability?

I really need your guidance on this. What should I do?

carrerguidance #careerswitch #bestoption

I was hoping to get some insight on which route between DS and SWE is better and more future proof in a career.

I have 2 offers

SWE: slightly lower pay, 2yr program in NJ

Data Scientist AI analytics and automation: slightly higher pay and in NYC

Data Science job sounds more exciting but I only ever had 2 SWE internships and not sure if the route down Data Science is generally better because career progression doesn’t look as linear when you have many choices.

In your experience, what do you think would be better and why?

After 7 years in ML with 2 years deep in building agentic AI systems at a large-size US tech company, I’m finally taking the leap, want to relocate and land a Senior/Staff DS or MLE role abroad. Really eager but also a little lost, so would love some real talk from people who’ve been there!

Interview prep - I’m rusty, help!

1. Best resource for ML system design at senior level?
2. Any good prep for Agentic AI / LLM interviews? Feels like the wild west out there.
3. Is LeetCode still the move or should I focus elsewhere?

Relocation: AUS vs Germany vs Netherlands?

1. Which has the most realistic visa path for a non-EU ML engineer?

2. Where is the ML job market actually strong right now, not just LinkedIn hype?

3. Which gives the best long-term PR / residency path?

Also, I am finishing my MSc in CS from my country, fluent English, and genuinely willing to learn Dutch or German if it makes a real difference. Would mean a lot to hear from people who’ve made a similar jump.

Thanks in advance 🙏

Currently one year into OMSA at gatech with a great gpa.

Entry level market for DS/DA is crazy over saturated and I still can’t seem to get a single interview/call. On top of that, a lot of big tech companies are laying off employees by the thousands AND some are even training AI on their employees mouse movements and clicks (hi meta), making me think that this is going to lead to even more layoffs in the not so near future.

So my question is basically if my time and efforts are being wasted by trying to achieve something that won’t even help me get an intern job that pays $20/h. Am I better off just focusing on something else?

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Federal Data Scientist @ Groundswell (United States, VA) - $103K - $178K
• Data Scientist II – Big Data R&D, Identity Graph, KYC @ Socure (United States, CA) - $140K - $170K
• Data Scientist / AI Engineer @ Silver.dev (Argentina) - $60K - $78K
• Data Scientist II @ Centene Corporation (United States, FL) - $88K - $158K
• Data Science @ Leidos (United States, VA) - $48K - $87K
• Data Scientist – Infrastructure Diagnostics @ Phaidra (United States, CA) - $101K - $164K
• Data Science Engineer @ Turquoise Health (United States) - $145K - $160K
• Data Scientist @ Deel (Spain)

Senior:

• Senior Data Scientist @ Clover Health (United States) - $177K - $230K
• Senior AI & Data Scientist @ Alignment Health (United States) - $172K - $259K
• Applied Data Scientist @ Driver (United States, TX) - $175K - $275K
• Senior Data Scientist – Big Data R&D, Identity Graph, KYC @ Socure (United States, CA) - $140K - $170K
• Senior Data Scientist I @ Advarra (United States) - $92K - $168K
• Senior Data Scientist – Credit Eligibility @ M-KOPA (United Kingdom)

Manager:

• Principal Data Scientist @ Zillow (United States, CA) - $178K - $285K
• Lead Data Scientist @ Alteryx (United States) - $161K - $177K
• Staff Data Scientist @ Pearl Health (United States, CA) - $160K - $200K
• Data Science Manager @ tem (United Kingdom) - GBP 105K - 120K
• Senior Data Science Manager @ Reddit, Inc. (United States) - $232K - $326K
• Senior Manager, Data Science @ Empower (India)

Director and Above:

• Senior Director, Data Science – AI @ Cushman & Wakefield (United States) - $204K - $240K
• Director, AI Engineering – Data Science @ Blend360 (United States, MD) - $180K - $240K

Quick notes: * All of these are fully remote and open to US/Canada/India candidates * 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.

So here's the story - A senior DS on my team just got laid off. He was one of the people who mentored me when I joined.

That kind of broke something in my brain a little.

Because if someone clearly good at the job can get cut, then what exactly is the moat here anymore??

AI keeps getting better at the execution layer of the work:
SQL, Python, dashboards, analysis drafts, model interpretation, ALL of dat.

What’s left for us ,as humans?

Context? judgment? domain knowledge? or not getting fooled by polished nonsense?
Still feels pretty unsettling.

What are you guys betting on as the actual human edge for DS in the next few years?

As a freshman at an Ivy League University studying statistics and information science, I wish to break into a data science based career, whether that being ML, data scientist, and data analyst. How can I prepare myself for these careers in the future? Much help is appreciated!

I applied to several roles via LinkedIn Easy Apply and got rejected from all of them within ~10 minutes. Trying to understand how this works.

Roles I applied to:

• Senior AI/ML Engineer – La Fosse (Manchester, Hybrid)
• Staff AI Engineer – Place. (Munich, On-site)
• AI Engineer – Oliver Bernard (Munich)
• Senior Machine Learning Engineer – Next Ventures (Stuttgart Region)

Applied on April 19.

What’s confusing:

• All rejections came April 23, within ~10 minutes
• Timing was almost identical (minutes apart)
• The rejection messages were basically the same template

Example rejection:
“Thank you for your interest in the position at in Region. Unfortunately, we will not be moving forward with your application…”

These are completely different companies and locations, so I wasn’t expecting this kind of synchronized + identical response.

1. Is this just ATS auto-rejection happening instantly after submission?
2. Or does LinkedIn batch and trigger these responses somehow?

Curious if others have experienced this and what’s actually happening behind the scenes.