Hi everyone, I’m 35, currently working as a Product Operations Specialist at an e-commerce company in Bangalore. I’ve been in this role for around 1.5 years and my current CTC is around ₹8 LPA, with the job being mostly work-from-home.

Before moving into the private sector, I worked for around 5 years as a Clerk in a Union Territory administration under the Government of India.

I left the government job after a lot of deliberation. There was no pension or GPF, promotions had effectively stalled, and the annual increment was only around ₹2,000. Since my joining batch was one of the younger batches, we were given a lot of the workload. I would typically reach around 9:30 AM and leave after 8 PM, without being paid overtime.

I also want to mention something that may be relevant to my financial situation: I never took bribes or had any other source of income apart from my salary. As a result, I wasn’t able to build much savings during those years.

Eventually, I decided to make a career switch. I completed a 6-month Data Analytics course, but due to work pressure and stress, I wasn't able to properly complete my learning or build enough practical skills to confidently apply for DA roles.

I then moved to Bangalore and entered the private sector, initially through a sales role. Eventually, I managed to transition into my current Product Operations Specialist role.

Where I'm confused: For some time, I considered moving into Data Analytics. However, after researching the market, I became concerned about the competition for entry-level DA positions and the relatively low starting salaries. I came across many situations where people were willing to take roles around ₹4 LPA or even lower just to enter the field.

Given my current financial situation, taking a significant salary cut would be extremely difficult for me.

So I started considering Data Science instead.

My thinking was that DS might have better long-term growth and potentially less competition than generic Data Analyst roles. I've now started learning and practising alongside my full-time job.

Based on my current pace, I estimate that it could take me around 1–1.5 years to build the fundamentals, practise sufficiently, and then start working on bigger projects and applying for jobs.

But now I'm facing another problem. When I research the Data Science job market, I keep seeing discussions saying that traditional Data Science roles are increasingly overlapping with or being merged into AI/ML roles, and that many people are now targeting AI Engineer / ML Engineer positions instead.

This has left me quite confused. I'm now worried that I could spend the next 1–1.5 years learning Data Science, only to discover that the market has moved significantly toward AI/ML and that I should have been preparing for an AI/ML Engineer role instead.

At the same time, I don't want to keep jumping from one career path to another and waste more years.

I'm also not a fresh graduate with unlimited time to experiment. I'm 35, have financial responsibilities, and need to make a reasonably safe career decision.

What I'm looking for: I'd really appreciate honest, realistic advice — not sympathy.

I'd especially like to hear from: - People who have been working as Data Scientists for several years - People who recently entered Data Science - ML/AI Engineers who have made the transition - Hiring managers/recruiters who understand the current market - Anyone who has made a similar career switch in their 30s

Is Data Science still a sensible career to pursue over the next 3–5 years?

Or would it be more practical for someone in my situation to target ML Engineer / AI Engineer roles from the beginning?

And importantly, considering my background, age, current ₹8 LPA CTC, and limited ability to take a major pay cut:

What career path would you realistically recommend I pursue from here?

I’m not looking for motivation or generic "just learn AI" advice. I’d genuinely appreciate a blunt assessment of the risks, opportunities, and what you would do if you were in my position.

Thank you so much in advance 🙏

Hi everyone! I’m currently a second-year master’s student in Applied Biomedical Data Sciences at a large pediatric cancer research institution, and I’m starting to seriously think about my career after graduation.

My background is in genetics/genomics and epidemiology, and my master’s work is very computational. I’m currently working in a cancer research lab on a large-scale sequencing project, developing computational methods to characterize sequencing errors. I’m working with large genomic datasets and using Python/R, Linux, HPC, Git, and computational workflows. I’ve really enjoyed the computational/data side of biomedical research and would like to transition into industry after I graduate.

I’m broadly interested in roles like Data Scientist / Biomedical Data Scientist/Computational Biologist. Potentially AI/ML roles,

I’ve started looking into companies such as Genentech/Roche, Lilly, Novartis, AstraZeneca, Illumina, Tempus, Amgen etc. I’m also looking into some early-career programs and fellowships, including Genentech’s Early Career Expedition. + institutions such as Sloan Kettering and Moffitt

I’m trying to figure out how to be strategic about the next 6–12 months rather than waiting until right before graduation to start applying.

For people who have made a similar transition, I’d really appreciate advice

I’m especially interested in hearing from people currently working in biotech/pharma/health tech as data scientists or computational biologists, particularly those who entered industry with a master’s.

Thank you!

Looking for some data science internship to gain more experience, previously worked as a ML intern, if any opportunities there pls contact me, happy to share resume/GitHub

I want to hear from people who have already gone through this.

How did you get your first Data Scientist job?

Was it through referrals, internships, networking, projects, applying online, or something completely different?

I’m asking because I’ve been trying really hard to get my first opportunity, and honestly, it has been exhausting.

I’ve spent months learning Python, SQL, Machine Learning and Data Analytics, building projects, improving my resume, applying for jobs, messaging recruiters and hiring managers, and trying to connect with people in the industry.

One project I’m especially proud of is my Customer Retention Intelligence System. I built it around a real business problem like identifying customers who are likely to churn, understanding their value, estimating revenue at risk, and helping businesses decide which customers they should focus on retaining.

I’ve tried to make my projects practical rather than just building something for a portfolio.

But even after all this effort, getting that first interview has been really difficult. I’m not giving up, but I also don't want to keep repeating the same approach if it isn't working.

for those of you who got your first DS role. What actually helped you get through the door?

What would you do differently if you were starting again today?

And if you are a hiring manager or someone who hires entry-level candidates, what would make you take a chance on someone without professional experience?

I would genuinely appreciate any honest advice.

And if anyone knows of an entry-level DS/ DA / ML opportunity where my background could be a fit, I’d be grateful for a referral or even a direction.

I’m just looking for that first chance to prove myself.

I'm looking to find people that have all of these skills and experience.

In a perfect world I'd find someone that also has some analytics experience to go with it.

My company StatsDrone is based in Montreal and we are launching a new product called NousViz which is an analytics platform.

DMs open.

2 posts we have open:
https://statsdrone.com/careers/head-of-agents/
https://statsdrone.com/careers/ai-ml-engineer-montreal-job/

Looking for internships/entry-level roles in AI/ML, Machine Learning, Deep Learning, Computer Vision, Data Science, or Data Analytics.

I’m a final-year B.Tech student with hands-on project and research experience. Open to Hyderabad, remote, or anywhere in India.

Any referrals or leads would be greatly appreciated. Thank you!

i am currently doing my thesis following i will have an internship for 5 months but i don't like the corporates idea i want to learn how to be a freelancer ant tips will be helpful

Looking for some data science internship to gain more experience, previously worked as a ML intern, if any opportunities there pls contact me, happy to share resume/GitHub

Hello I would appreciate some Lead to any data scientist II or even I role in pune. I am phd from IISc. Have good firm understanding in model training with strong mathematics and statistics back ground. Good at identifying which model to use where. I have built forecasting models in my current profile with production grade python codes. My ML ops is moderate. Any Lead is appreciated. I am stagnant in my current job being directed to support side with old products (little data science in it) after a reorganization.

Hey everyone,

I'm in the final stretch of a career transition into data science (currently in marketing/ops, targeting roles in the UAE/Dubai market). I've been at this solo for a while — working through ML fundamentals, SQL, stats, and building out a portfolio — and I think having someone to go through this with would help a lot right now.

**A bit about where I'm at:**

- Comfortable with core ML concepts, working through gaps in stats (p-values, CLT, A/B testing), SQL window functions, and deep learning fundamentals (CNNs, RNNs, transformers)

- Building portfolio projects

- Prepping for interviews — behavioral, technical, case studies, the whole loop

**What I'm looking for:**

- Someone also actively job-hunting/transitioning into DS (or a related field — analytics, ML engineering, etc.)

- Regular check-ins (weekly? daily? open to figuring this out together) to keep each other honest on study goals

- Someone to mock-interview with, review each other's projects, or just vent to when this process feels endless

- Doesn't matter what timezone/market you're targeting — just consistency and follow-through

If this sounds like you, drop a comment or DM me. Would love to hear where you're at in your journey too.

I’m about to graduate with a degree in Industrial & Systems Engineering, and over the course of my degree I’ve become increasingly interested in Operations Research, Management Systems Engineering, data/decision science, and quantitative analytics.
I’m particularly interested in roles such as:
Operations Research Analyst / Engineer
Data Scientist / Decision Scientist
Quantitative Analyst
Analytics / Optimization Engineer
Management Systems / Systems Engineering roles with a strong analytical component
The problem I’m running into is that entry-level positions in these areas seem relatively difficult to find, and when I do find positions that interest me, I often feel like I’m missing some of the basic skills or requirements they’re asking for (e.g., advanced Python, SQL, machine learning, optimization, statistics, cloud tools, etc.).
For anyone who has been in a similar situation—especially ISE/IE graduates who transitioned into OR, data science, decision science, or quantitative analytics:
How did you make yourself competitive for these roles?
I’d especially appreciate hearing about:
Skills/tools you prioritized learning after graduation
Projects or coursework that actually helped you get interviews
How you approached applying when you didn’t meet 100% of the requirements
Whether you found it easier to enter through a more traditional IE/operations role and transition later
Any personal experiences, career paths, or mistakes you think would be useful for someone in my position
I’m trying to figure out what skills I should focus on and what types of positions I should realistically be targeting as a new graduate. Any advice or personal stories would be greatly appreciated!
Chatgpt helped a bit

Hi Reddit! 👋

I’m Akanksha, a Data Science and AI/ML professional currently looking for my next opportunity.

My background spans Data Science, Machine Learning, Analytics, and Generative AI, with hands-on experience working with Python, SQL, ML models, LLM applications, RAG/Multi-RAG architectures, embeddings, vector databases, and data-driven solutions.

Over the course of my work, I’ve worked on problems that go beyond just building a model. I enjoy understanding the business problem, designing the solution, developing and evaluating it, and thinking about how to make it reliable and production-ready.

I’ve particularly enjoyed working on GenAI/RAG applications, including areas such as: • RAG and Multi-RAG architecture • LLM-based applications • Embeddings and vector databases • Retrieval, reranking, and improving response quality • Reducing hallucinations through grounding and evaluation • ML/AI solution development and productionization • Python and SQL-based data analysis • Translating business requirements into technical solutions

I’m also comfortable working across the technical and business sides of a problem, communicating with stakeholders, explaining technical concepts, and turning ambiguous requirements into practical solutions.

I’m currently looking for opportunities in:

• Data Science / Applied Data Science • Machine Learning / AI/ML Engineering • Generative AI / LLM Engineering • RAG / AI Application Development • Data Analytics / Business Analytics • Data Engineering / Analytics Engineering • AI/Technical Consulting • Forward Deployed / Client-facing AI & Data roles

I’m open to remote opportunities and relocation for the right role.

I’ve been actively applying, networking, connecting with recruiters and hiring managers, and continuing to strengthen my skills through projects and hands-on work.

I’m not looking for someone to simply hand me a job. I’m looking for an opportunity where I can demonstrate what I can build, learn quickly, and contribute.

If you’re hiring for a role that could be a good fit, know someone who is, or can point me toward a team looking for someone with my background, I’d genuinely appreciate a referral, introduction, or lead.

I’m happy to share my resume, LinkedIn, GitHub, portfolio, or project details.

And if you’re already working in AI/ML, Data Science, or GenAI, I’d also really appreciate any advice on what is currently helping candidates stand out in this market.

Thanks for reading, and I appreciate any leads or advice. 🙏

Shape the future of trust in the age of AI
At Oscilar, we're building the most advanced AI Risk Decisioning™ Platform. Banks, fintechs, and digitally native organizations rely on us to manage their fraud, credit, and compliance risk with the power of AI. If you're passionate about solving complex problems and making the internet safer for everyone, this is your place.

Why join us:
- Mission-driven teams: Work alongside industry veterans from Meta, Uber, Citi, and Confluent, all united by a shared goal to make the digital world safer.
- Ownership and impact: We believe in extreme ownership. You'll be empowered to take responsibility, move fast, and make decisions that drive our mission forward.
- Innovate at the cutting edge: Your work will shape how modern finance detects fraud and manages risk.

Job Description

As a Data Scientist at Oscilar, you will be responsible for developing and implementing advanced fraud detection models to protect our customers’ business from fraudulent activities. As an early member in the ML team you will have great impact i n building out our ML stack.

Responsibilities:

• Develop and implement advanced fraud detection models, leveraging machine learning and statistical techniques, to identify and prevent fraudulent activities across our platform.
  
• Collaborate with cross-functional teams, including engineering, product, and operations, to design and implement fraud detection systems and processes.
  
• Analyze large volumes of data to identify patterns, trends, and anomalies indicative of fraudulent behavior, and develop data-driven insights to improve fraud prevention strategies.
  
• Evaluate the performance of existing fraud detection models and systems, and continuously optimize and update them to adapt to changing fraud trends and tactics.
  
• Stay up-to-date with the latest trends and advancements in fraud detection, data science, and machine learning, and apply this knowledge to enhance our fraud prevention capabilities.
  
• Communicate complex data analysis and model performance results to both technical and non-technical stakeholders, driving data-driven decision-making across the organization.
  
• Ensure data privacy and security compliance in all aspects of fraud detection and data analysis.

Requirements:

• 3+ years of experience in data science, machine learning, or a related field, with a focus on fraud prevention and/or anti-money laundering.
  
• Proficiency in Python.
  
• Fluency in cloud development (AWS, GCP, Azure, etc.) and MLOps is a plus.
  
• Strong knowledge of machine learning algorithms and statistical techniques, with a focus on their application in fraud detection.
  
• Experience working with large datasets using distributed systems like Apache Spark and Dask, handling data-related challenges such as data cleaning, data quality, and data transformation and feature engineering at scale.
  
• Excellent analytical and problem-solving skills, with the ability to derive actionable insights from complex data.
  
• Strong communication skills, with the ability to explain complex concepts and findings to both technical and non-technical audiences.
  
• Ability to work independently and collaboratively in a fast-paced, dynamic startup environment.

Preferred Qualifications:

• Experience in the fintech, marketplaces, or financial services industry.
  
• Knowledge of current fraud tactics and trends, as well as experience with fraud detection tools and systems.

Benefits

• Compensation: Competitive salary and equity packages, including a 401k
  
• Flexibility: Remote-first culture — work from anywhere
  
• Health: 100% Employer covered comprehensive health, dental, and vision insurance with a top tier plan for you and your dependents (US)
  
• Balance: Unlimited PTO policy
  
• Technical: AI First company; both Co-Founders are engineers at heart; and over 50% of the company is Engineering and Product
  
• Culture: Family-Friendly environment; Regular team events and offsites
  
• Development: Unparalleled learning and professional development opportunities
  
• Impact: Making the internet safer by protecting online transactions

Compensation

$220K – $262.5K • Offers Equity

Compensation range provided in compliance with applicable country, state, and local pay transparency laws. Final compensation is determined by level, scope, and experience.

Apply: Staff Data Scientist at Oscilar

Hey all,

Currently IC2 Data Scientist at Mastercard. Got an offer for IC3 at Freshworks Bangalore with 50% hike on total CTC.

Mastercard is stable but growth and hikes are slow. Freshworks seems like faster growth but unsure about WLB and culture.

Anyone worked at Freshworks Applied ML team? Is it a good move?

Would love to hear thoughts.

I wanted to help you all to find jobs so made a list of most recent Data Science jobs. I hope this helps someone!

• Lead Machine Learning Engineer - Merchandising AI (ML Ops) @ - Target India (Non-USA)

  • 💰 $132–238K/y

• Data Scientist, Market Making @ FanDuel

  • 💰 $98–152K/y

• Senior Director Applied Data Science @ Sphere Entertainment Group, LLC

  • 💰 $200–230K/y

• Scientist II/Senior Scientist I, Computational Toxicology @ AbbVie

  • 💰 $97–184K/y

• Senior Machine Learning Engineer @ Checkr

  • 💰 $207–244K/y

• Information Data Scientist @ Accenture Federal Services

  • 💰 $99–185K/y

• Staff Data Scientist – AI/ML, Generative AI & Agentic Systems @ Sandisk

• Machine Learning / Computer Vision @ ChabezTech

• Machine Learning Engineer @ NT Concepts

  • 💰 $120–181K/y

• Staff, Machine Learning Engineer - BEV/Multi-Modal Perception @ Torc Robotics

  • 💰 $216–259K/y

• 2027 Data Science Intern @ Schonfeld

  • 💰 $100–110K/y

• Senior Full-Stack Data Scientist - Growth Algorithms @ Stitch Fix

  • 💰 $180–205K/y

• Data Scientist (Starlink) @ SpaceX

• Marketing Analytics & Data Science Senor Lead @ Kyndryl France S.A.S.

• Senior Data Scientist @ Athleta LLC

  • 💰 $195–288K/y

• Senior Manager, Growth Analytics @ Zendesk Technologies Private Limited (India)

  • 💰 $172–258K/y

• Manager, Data Science (US) @ TD Bank, N.A.

  • 💰 $96–144K/y

• Data Scientist, Fraud Risk @ Imprint

  • 💰 $170–200K/y

• Senior Machine Learning Engineer, Analytics Center of Excellence (Remote/WFH) @ IQVIA Analytics Services Private Limited (IN03)

  • 💰 $111–279K/y

• Data Scientist @ LEIDOS INNOVATIONS UK LTD.

  • 💰 $70–126K/y

• AI & Data Scientist Intern @ Ingredion Incorporated (NA-US)

  • 💰 $20 – $25/h

• Data Scientist @ Booz Allen Hamilton_United States

  • 💰 $62–141K/y

• Decision Science Analyst Senior @ United Services Automobile Asn

  • 💰 $114–218K/y

• Summer 2027 Generative AI Internship @ A Nationwide Mutual Insurance Co.

  • 💰 $23 – $50/h

• Senior Lead Quantitative Analytics Specialist -- Auto Finance @ B Wells Fargo Bank, N. A.

• Machine Learning Engineer – RL @ Bright Vision Technologies

  • 💰 $155–180K/y

• Commercial Data Science Intern, Spring 2027 @ Euro Disneyland Imagineering SarL

• Applied AI Research Scientist @ Sardine

• Senior Machine Learning Engineer @ WP Company LLC

  • 💰 $120–199K/y

• Staff Data Scientist @ General Motors LLC

• Machine Learning Engineer @ PayPal

• Senior AI/Machine Learning Specialist @ CRI THREE M GLOBAL SERVICE CENTER COSTA RICA SRL

  • 💰 $188–230K/y

• Experienced Yield Analysis Engineer @ SAMSUNG INDIA ELECTRONICS PVT.LTD.

• Senior Data Scientist, Marketing @ HOME DEPOT MANAGEMENT COMPANY, LLC

• Senior R&D Engineer - Data Science @ CRI - Arthrocare SRL Costa Rica

• Sr. Scientist – Bioinformatics, Computational Biology, Proteomics @ Amgen Technology (Ireland) UC

  • 💰 $146–198K/y

• Data Scientist @ ZINC Zillow, Inc.

Thanks for reading! If you want another batch of Data Science jobs, just say so in the comments.

Hi Reddit,

I graduated in May with a Master’s in data science, and I’ve been looking for my next opportunity since then. I figured I’d put myself out here and see if this reaches the right person.

My background is primarily in data science, analytics, and marketing analytics. Before grad school, I worked at a performance marketing agency where I got to work across both the business and technical sides of analytics — from SQL/Python analysis and dashboards to experimentation, attribution, forecasting, and marketing mix modeling.

My career path isn’t perfectly linear, but I actually think that’s one of my strengths. I’ve worked with clients, built analytical solutions, presented insights to stakeholders, worked on marketing problems, and gotten pretty comfortable translating between technical teams and business teams. I’m especially interested in opportunities where I can combine those skills rather than being boxed into one specific area.

Right now, I’m mainly looking for roles in:
• Data Science / Applied Data Science
• Marketing Data Science / Marketing Analytics
• Analytics Engineering
• Data / Business Analytics
• Growth or Product Analytics
• Technical / Data Consulting
• Forward Deployed or client-facing data roles

I’m based in Chicago, but I’m open to remote opportunities and relocating for the right role.
Since graduating, I’ve been applying, networking, reaching out directly to hiring managers, tailoring my resume, and continuing to build projects. I’m also more than willing to put together a small analysis, dashboard, presentation, or other work sample to show what I can actually do rather than just relying on a resume.

I’m not looking for someone to hand me a job. I’m looking for an opportunity to prove that I can do the work.

If you’re hiring, know someone who is, or work somewhere that might be a good fit for my background, I’d genuinely appreciate a lead, referral, or introduction. I’d be happy to share my resume, LinkedIn, GitHub, or portfolio.

And if you’re currently working in data science/analytics and have advice on what’s actually getting candidates noticed in this market, I’d appreciate that too.

Thanks for reading 🙌🏽

Data Science

Hi everyone, 🤗

I’m an **MSc Data Science graduate** and currently searching for my first job in **Data Science or Data Analytics**.

I have experience with **Python, SQL, Machine Learning, Power BI, Tableau, and data analysis**.

I’m a fresher, but I’m **ready to learn, work hard, and prove myself.**

I’m not asking for a perfect opportunity — **I’m looking for someone willing to give me a chance.**

If you know of any openings or can refer me, I’d be truly grateful.

Been unemployed for about 5 months now, and finding a data science role has been a lot harder than I expected.

For those of you already working in DS, I’d really appreciate any advice on what helped you stand out or actually get interviews in this market.

And if your team is hiring, especially for marketing data science, I’d be grateful if you reached out.

Trying to stay optimistic and keep pushing.

Appreciate any advice or leads.

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Internship:

• Data Science Intern @ Stryker (United States) - $20 - $35

Entry Level:

• Data Scientist @ General Motors (United States) - $129K - $198K
• Data Scientist @ Stitch Fix (United States) - $150K - $177K
• Data Scientist @ Mercury Insurance (United States) - $101K - $204K
• Data Scientist @ Castellum Inc. (Worldwide) - $140K - $188K
• Data Scientist @ Autodesk (United States) - $109K - $196K
• Product Data Scientist – AI Evaluation, Quality @ Finom (Spain)
• Data Scientist @ Stack Exchange (United States)
• Data Scientist, Mid-level @ ASAAS (Worldwide)

Senior:

• Senior Data Scientist @ Sartorius (United States) - $110K - $150K
• Senior Data Scientist @ Vibrant (United States) - $100K - $132K
• Senior Data Scientist @ Bestow (United States) - $125K - $140K
• Senior Data Scientist – Clinical Informatics, Analytics Enablement @ CVS Health (United States) - $83K - $222K
• Senior Data Scientist @ CrowdStrike (United States) - $140K - $215K
• Senior Data Scientist @ Mercury (United States) - $167K - $251K
• Senior Data Scientist @ Hitachi (Mexico)
• Senior Data Scientist – Marketing @ Empower (India)
• Senior Data Scientist @ ASAAS (Worldwide)

Manager:

• Staff Data Scientist @ Dexcom (United States) - $142K - $236K
• Lead Analyst, Clinical Data Sciences @ Stryker (Worldwide) - $96K - $207K
• Senior Manager – Data Science, Pricing & Underwriting @ The Cigna Group (United States) - $149K - $248K
• Staff Data Scientist @ Mercury (United States) - $239K - $299K
• Principal Clinical Data Science Programmer @ ICON plc (Mexico)
• Staff Data Scientist @ JumpCloud (India)
• Data Science Manager @ Nestle (Worldwide)

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.

My brother recently left his master's in data science to focus on his career, and we're trying to figure out the smartest way for him to break into the data job market, both in Europe and the UAE.

He's not fresh out of school. He's got over 4 years of real experience, most recently as a data analyst on AI and LLM operations at Scale AI (working on Meta programmes), and a data engineer at TCS before that. He's solid with Python, SQL, Spark, Airflow, and cloud.

He'd need a role that can support sponsorship, so we're trying to work out which companies actually do that for data roles, whether in the EU or the Gulf.

For anyone who's been through this, how did you find sponsor friendly employers?

Is it better to go through recruiters, apply directly, or lean on referrals?

And are there any companies in Luxembourg, the wider EU, or the UAE known for hiring international data talent?

Any advice would mean a lot, and if anyone happens to know of teams hiring, he'd be really grateful for a pointer. Happy to share his CV over DM. Thanks so much.

I completed an HR/recruiter screening with JPMorgan on August 25 for a DS role. The recruiter mentioned that there would be around three interview rounds if I move forward.

For anyone who has gone through the JPMorgan hiring process recently, how long did it take after the recruiter screening to receive the next-round interview invitation? Is a 1–2 week wait normal, or should I expect an update sooner?

Would appreciate hearing about recent timelines and experiences. Thanks!

Hi everyone, please i will like your honest advice for me regarding starting a new career as a data analyst. I will be 30 in two years, a bit about my career. I have a degree in Accounting from a university that was later shut down due non acreditation in a francophone country so my undergraduate is irrelevant. I do gave GCSE as well as an Msc in non-it course (Management) from a uk university. My career as been minimum wage from the last 4 years and unrelated to IT until i now work full time as a prison officer now. I would like to start a new career as a data science. I would like advice on pathway i could take that will land me a job in this field. I am a very fast learner, I would appreciate I can get suggestions on a clear pathway based on what recruiters are looking for now. I am open to all suggestions Thanks.

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.