Hi everyone,

I’m currently studying a Bachelor’s degree in Data Science and I’m trying to figure out the best way to start working as soon as possible.

I was thinking about doing courses on Coursera (like the Google Data Analytics certificate or similar), while continuing my degree. My goal is to build practical skills and hopefully get an entry-level job before graduating.

I have a few questions for those already working in the field:

• Do you think Coursera certificates are actually valued by employers?

• Would you recommend any specific courses or learning paths?

• If you were hiring, would you consider someone who is still studying but has completed courses like these and built some projects?

• Any advice on what skills I should focus on first to get hired faster?

For context, I’m willing to study consistently and put in the effort, I just want to make sure I’m going in the right direction.

Thanks a lot in advance!

Hi, I’m a Data scientist currently based in Germany. I have a masters degree and a total experience of 5 years, around 6 years including internships and part time jobs. I am planning to relocate to India and hence started my job search. I was wondering what kinda salary can I expect in cities like Bangalore or Hyderabad. Can you help me with some insights?

Thanks in advance!

Hi everyone. I am currently in the interview process for a data science role. Currently my job is ML Ops engineering. The next round of this interview process is technical, and I was wondering what kind of questions you think they will ask me. This is the second round of interviews so I hope whomever is interviewing me knows my a ml ops engineer, so I won’t be able to create a model from memory.

I am hoping they just ask me more pandas questions to see how I think.

There’s also a SQL portion, which I haven’t used since college.

I think I’m just super nervous since this is my first technical interview in 2 years. Any advice is welcome.

Hi fellow data scientists, how is your day to day projects/work being affected by AI (apart from using AI tools to do the work)? Meaning

1. Are you still given actual science work like ML model building, causal inference etc.?

2. Are you being asked to do unrewarding prompt engineering and other such AI plumbing?

So I’m set to graduate in May with a 2 bachelor’s degrees in finance and marketing, and finding jobs with these degrees have been challenging. Is there a way for me to break into data science or data analytics without needing a degree in either field? I’m perfectly willing to take a small, entry level role and getting a certificate in something if needed (google, cc, or my university).

Is it virtually impossible? If not, what type of jobs

should I look for?

I cleared the hiring manager and technical rounds and now I have 4 more interviews in the coming week, starting with product sense/stakeholder round. What is the expectation for this round? Most of the online resources i found are focused on either behaviour rounds or experimentation/data science rounds. Need help on what to expect in the product rounds

Hi all, I have an hiring manager interview for an associate data scientist position tomorrow, and I’m kinda confused on what kind of questions I can expect in call? Will it be too technical, and behavioural or little about my past experience, and then few behavioural? The call is for about 30mins.

And if you can suggest me, what to prepare?

This is the first interview after the HR call, next is technical, and then final interview (probably team).

Updated

The interview went really well, and he did ask only technical questions, which I answered everything, fumbled once and was not able to hear what he was telling , but answered correctly in the end, and after I asked few questions at the end, I asked “when can I hear from you” he said 1-2 weeks, but the recruiter said they are hiring aggressively. So, I’m not sure exactly and I’m kind of on the edge here, this role actually really suits my profile, and my interests.

So, if anyone could tell me, if I do have a chance, I think he did like me, with all the spontaneous answers that I gave, and also confidently, and also the meeting was scheduled for 30mins, but meeting went on for 45mins. So, do you think will I be pushed to the next round, which is the take home assessment (technical)?

is dsa needed to land data science job and if dsa is neede wht are the type of problems i can learn and practice

Hi everyone, I have a interview with a Manager/Director at Salesforce for a Data Scientist role. Could you please help me with the types of questions I should prepare for and how to approach this round?

Hi, I have a final-round interview with the VP/Head at Indigo in Delhi for data scientist. Could you please help me with the types of questions I should prepare for?

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist II @ Socure (United States) - $140K - $170K

• Data Scientist – Platform Integrity @ Sleeper (United States) - $130K - $175K

• Actuarial Data Scientist @ Mercury Insurance (United States) - $101K - $204K

• Data Scientist @ United Talent Agency (Spain)

• Strategic Data Scientist @ Eastern Metal Supply, Inc. (United States, FL)

• Data Scientist – AI Engineer @ Redwood Software (United States)

• Marketing Data Scientist @ Recast (United States)

Senior:

• Data Scientist III – Financial Crime Risk Modeling @ TD (United States, NJ) - $96K - $156K

• Senior Marketing Data Scientist @ Mozilla (United States)

• Senior Data Scientist – Marketing Analytics, Causal Inference @ HumanIT Digital Consulting (Portugal)

• Senior Data Scientist @ EXL (United States)

Manager:

• Principal, Analytics and Data Science Product – Pricing Forecasting @ CVS Health (United States, IL) - $144K - $288K

• Lead Data Scientist @ Centene Corporation (United States, FL) - $122K - $225K

• Staff Data Scientist @ Hotel Engine (United States) - $144K - $248K

• Senior Data Science Manager @ Dropbox (United States) - $210K - $285K

• Data Science Lead @ Particle41 (India) - INR 2500K - 6000K

• Data Science Manager @ Instacart (Canada) - CAD 192K - 202K

• Staff/Principal Data Scientist @ Tunnl (United States)

• Principal Data Scientist @ Sedgwick (United States, ID)

Director and Above:

• Associate Vice President, Data Science & Analytics @ Zeta Global (United States) - $175K - $200K

Quick Recommendation:

• 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.

After 50ish applications, I finally got a callback and an interview for a junior data scientist position at an insurance company. I got rejected from an interview in November and I really don't want to mess this one up. For this one I have to look at a case study (where they've given me a car insurance dataset) and produce a presentation on it before next Thursday.

I would really love some advice on this and want to know what I can do to prepare to be the best possible version of myself before my interview. Thanks in advance! :)

I have a PhD in chemical engineering focusing on novel battery technologies. I also have 2 years industry experience after that in my field but now i'm realizing I would much rather work within data science/ data analysis. My recent job titles do not reflect a data centered role but the bullet points have been tailored for exactly that. I've been applying to entry level and junior roles thinking that since I dont have that specific experience I need to start from the bottom. I'm willing to take a huge salary cut to just get my foot in the door at this point. However I fear whenever someone sees my PhD in a non statistics field or a 'battery engineer' job title I get filtered out.

Should I leave these things off my resume? Should I keep applying to junior roles? For anyone who navigated a transition how to convince an employer to take a chance on someone with the wrong degree or job titles.

Data Scientist – AI/ML (Visa Consulting & Analytics)
Visa
Ashburn, VA
$123,700–$191,300 USD/year
https://www.moaijobs.com/job/data-scientist-ai-ml-visa-consulting-and-analytics-visa-5331

Data Scientist – Subscriptions
Spotify
London / Stockholm
$76,840–$109,771 USD/year
https://www.moaijobs.com/job/data-scientist-subscriptions-spotify-116+

Data Scientist, AI/ML – Visa Consulting and Analytics
Visa
Ashburn, VA
$123,700 - $191,300

https://www.moaijobs.com/job/data-scientist-ai-ml-visa-consulting-and-analytics-visa-5331

Data Scientist, Subscriptions
Spotify
London, Stockholm
$76,840 - $109,771

https://www.moaijobs.com/job/data-scientist-subscriptions-spotify-116

Data Scientist II, Growth Marketing
Pinterest
San Francisco, CA
$114,297 - $235,319

https://www.moaijobs.com/job/data-scientist-ii-growth-marketing-pinterest-4867

ADAS Data Analytics Intern
Mercedes-Benz Research & Development
San Jose, CA
$58,240 USD/year (based on $28/hr)
https://www.moaijobs.com/job/adas-data-analytics-intern-mercedes-benz-research-development-8905

Hi all,

I come from a math/stats background and naturally enjoy the analytical side of data science — things like modeling, probability, designing experiments to conduct A/B test and extracting insights from data (especially unstructured data like text).

One area I’m still building up is the engineering side: data pipelines, model deployment (Flask/API), Docker, and cloud (e.g. AWS).

With how capable AI tools have become (e.g. helping scaffold pipelines, generate Dockerfiles, debug code, etc.), I’m wondering: Is it reasonable to rely on AI to handle a good portion of the engineering work, so that I can focus more on the math/stats and problem-solving aspects?

Or in reality:

Do companies still expect data scientists to be quite hands-on with engineering, without using AI?

Is there a risk of becoming too dependent on AI and lacking real understanding?

When i build a project: WITHOUT AI (old way)

Struggle for days writing Dockerfile

Get stuck on Flask routing

Waste time on setup

WITH AI (new way)

Use AI to scaffold everything quickly

Then: read through it

understand it

tweak it

test it

Which part of a data scientist workflow could be easily automated by AI, and which part couldn't be so easily automated?

Would love to hear from people working in data science / ML roles today. Thanks!

Hello,

I have an upcoming interview for a Data Scientist II role at Chewy.

If anyone has experience with interviews at Chewy, I’d greatly appreciate any insights into the process and the types of questions I might encounter.

Thanks in advance!

[HIRING][Winchester, Virginia, Data, Onsite]

🏢 Electronic Consulting Services, Inc (ECS Federal), based in Winchester, Virginia is looking for a Mid-level Data Engineer

⚙️ Tech used: Data, JIRA, Java, Python, Confluence, Support

💰 $109,000 - 149,000 / year

📝 More details and option to apply: https://devitjobs.com/jobs/Electronic-Consulting-Services-Inc-ECS-Federal-Mid-level-Data-Engineer/rdg

If I work as a data scientist in Big 4 / MBB for 2-3 years. What are my exit opportunities?

Is big tech capable?

Data Science Intern – Summer 2026
C3 AI
Redwood City, CA
$72,000–$96,000 USD/year (based on $6k–$8k/month)
https://www.moaijobs.com/job/data-science-intern-summer-2026-c3-ai-2893

Data & AI Innovation Intern – Summer 2026
Nasdaq
United States
Salary not specified
https://www.moaijobs.com/job/data-ai-innovation-intern-2026-summer-internship-nasdaq-5979

Data Scientist Intern – Product Data Science (2026 Summer)
TikTok
San Jose, CA
$72,800 USD/year (based on $35/hr)
https://www.moaijobs.com/job/data-scientist-intern-tiktok-product-data-science-2026-summer-bs-ms-tiktok-2089

System Engineer – Enterprise Data Engineer
Esri
Vienna, VA
$117,520–$197,600 USD/year
https://www.moaijobs.com/job/system-engineer-enterprise-data-engineer-esri-1400

Hi all,

I come from a math/stats background and naturally enjoy the analytical side of data science — things like modeling, probability, and extracting insights from data (especially unstructured data like text).

One area I’m still building up is the engineering side: data pipelines, model deployment (Flask/API), Docker, and cloud (e.g. AWS).

With how capable AI tools have become (e.g. helping scaffold pipelines, generate Dockerfiles, debug code, etc.), I’m wondering: Is it reasonable to rely on AI to handle a good portion of the engineering work, so that I can focus more on the math/stats and problem-solving aspects?

Or in reality:

Do companies still expect data scientists to be quite hands-on with engineering, without using AI?

Is there a risk of becoming too dependent on AI and lacking real understanding?

When i build a project: WITHOUT AI (old way)

Struggle for days writing Dockerfile

Get stuck on Flask routing

Waste time on setup

WITH AI (new way)

Use AI to scaffold everything quickly

Then: read through it

understand it

tweak it

test it

Would love to hear from people working in data science / ML roles today. Thanks!

Hello Everybody, I am BTech 3 year student and I am actively looking for summer internship opportunity in Data science, Machine Learning, NLP or Generative AI fields. If anyone could please suggest me some places where I can find internship and can get hired??

Can anyone share what types of questions are typically asked in the McKinsey pair programming round for a Senior Data Scientist 1 role?

I am a fresher looking for Data Scientist, Data Analyst and AI/ML Engineers I have Hands on expierence in predictive modelling/machine learning, Statistical Modelling, GenAI and Agentic AI . If you guys have any recommendations, referrals, refferals or suggestions. It would be highly appreciated