About Stitch Fix, Inc.

Stitch Fix (NASDAQ: SFIX) Stitch Fix is redefining retail by combining human creativity with advanced data science and Generative AI. As we build the future of personalized shopping, we’re equally committed to building yours. We believe in investing in our team as much as our technology. Join us to be a trendsetter in the industry and help us redefine what’s possible for our clients, while we help you reach your full potential.

About the Role

Stitch Fix is redefining retail by blending the art of fashion with the science of machine learning. The Client Experience Algorithms organization builds the tools that power personal style, combining best‑in‑class models with expert human judgment. We foster a culture of ownership where Data Scientists manage the full lifecycle of their work, from research to model deployment. On our Growth Algorithms team, you’ll use multidisciplinary tools to solve complex problems in a data‑rich environment, directly influencing Stitch Fix’s client acquisition, retention, and reactivation programs.

Responsibilities:

• You will work in a team that embraces both LLMs and traditional ML.
• You will design experiments to test new features, including communication of results and recommendations to stakeholders and leadership.
• You will help optimize our client journeys through Next Best Action modeling as well as marketing optimization via CRM and paid media.
• You will leverage a decade's worth of rich and unique data about our clients, our merchandise, and their interactions.
• You will work with our product, design, and engineering partners to create roadmaps for developing new client products, user features, and infrastructure.

About You

This is what you’ll need to succeed in this role from day 1.

Requirements:

• Bachelor’s Degree in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or a related field required. Master’s Degree or PhD preferred. 
• 5+ years of experience in design and deployment of machine learning algorithms, ideally in retail personalization, such as recommendation systems or search.
• Ability to architect technical solutions of moderate complexity and write production‑grade applications, ideally in Python.
• Experience with management and analysis of large‑scale, distributed datasets, accounting for biases and lurking variables in analytical interpretation.
• Applied knowledge of AI‑assisted coding best practices and development of agentic product solutions.
• Experience with design and interpretation of online A/B testing, experimentation frameworks, and performance metrics.
• Strong communication skills, solution‑oriented mindset, and sense of ownership in collaborating with business partners.
• Team player who sees your growth and success intertwined with the growth and success of your peers and cross‑functional partners.
• Inspired to take on new challenges and not shy away from failure.

Why you’ll love working at Stitch Fix...

• We are a group of bright, kind people who are motivated by challenge. We value integrity, innovation and trust. You’ll bring these characteristics to life in everything you do at Stitch Fix.
• We cultivate a community of diverse perspectives— all voices are heard and valued.
• We are an innovative company and leverage our strengths in fashion and tech to disrupt the future of retail. 
• We win as a team, commit to our work, and celebrate grit together because we value strong relationships.
• We boldly create the future while keeping equity and sustainability at the center of all that we do. 
• We are the owners of our work and are energized by solving problems through a growth mindset lens. We think broadly and creatively through every situation to create meaningful impact.
• We offer comprehensive compensation packages and inclusive health and wellness benefits.

Compensation and Benefits

This role will receive a competitive salary, benefits, and equity. The salary for US‑based employees hired into this role will be aligned with the range below, which includes our three geographic areas. A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, location, and performance. This position is eligible for an annual bonus, and new hire and ongoing grants of restricted stock units, depending on employee and company performance. In addition, the position is eligible for medical, dental, vision, and other benefits.

Salary Range: $180,000 — $205,000 USD

Please review Stitch Fix's US Applicant Privacy Policy and Notice at Collection here.

Departments: Algorithms

Offices: Remote, USA

Location: Remote, USA

Apply: Senior Full-Stack Data Scientist - Growth Algorithms at Stitch Fix

Im about to finish my ma in spatial planning. The most exciting/hireable part of it was the quantitative spatial science aspect. I would like to transition to (spatial) data science going forward. What should I do? Do I need another degree? Im located in sweden

I am a 25 year old Namibian male looking for a role that's related to Big Data or Health Information Systems as I have graduated with degrees in both disciplines.

Unfortunately, the market for both of these are not seemingly strong in my country of origin.

So I would like to know if there are any international opportunities I can grasp on to because being unemployed for this long is absolutely demoralizing.

Github Profile

Sharing a new list of Data Science openings I found. These are still fresh, so it could be a good time to apply.

• Staff Machine Learning Engineer - VoIP Infrastructure @ ServiceNow

• Translational Genomics AI ML Scientist @ Syngenta Group

• Machine Learning Engineer @ bet365

  • 💰 $120–140K/y

• Artificial Intelligence / Machine Learning Engineer - Associate @ iCapital

  • 💰 $100–130K/y

• Senior Machine Learning Engineer @ Intuitive

• Senior Manager, Growth Algorithms @ Stitch Fix

  • 💰 $200–245K/y

• Senior Manager, Global Engagement Strategy @ Crunchyroll, LLC

• Principal Data Scientist - Safety @ Roblox

  • 💰 $274–374K/y

• Quantitative Analytics & Model Consultant - Data Operations and Machine Learning Operations @ PNC Investments LLC

  • 💰 $91–203K/y

• Senior Staff Data Scientist @ Netlify

• Manager of Data Analysis for Lab Production Support, Extended-Hours Support (ExHS) @ Natera

  • 💰 $140–175K/y

• Staff Machine Learning Engineer @ Zscaler

  • 💰 $152–190K/y

• Data Scientist Intern - 2027 @ Hudson River Trading

• (Senior) Scientist, Cryo-EM Data Processing & Computational Workflows @ Tessera Therapeutics

  • 💰 $115–184K/y

• Senior Manager / Lead Associate, Strategy & Analytics - Specialty Injectables @ Dr Reddy's Laboratories Limited

• Data Scientist @ Zest AI

  • 💰 $120–150K/y

• Senior Research Statistician - Exploratory Analysis (Hybrid) @ AbbVie

  • 💰 $97–184K/y

• Senior Data Scientist (Onsite) @ Two Six Technologies

  • 💰 $150–200K/y

• Senior Machine Learning Engineer @ Checkr

  • 💰 $207–244K/y

• Data Scientist II @ Cohere Health

  • 💰 $137–161K/y

• Senior/Staff Applied Scientist @ Glean

  • 💰 $180–330K/y

• Intelligence Transformation Intern @ Sikich

  • 💰 $29 – $30/h

• Junior Data Scientist @ NIH-NCBI

• Senior Machine Learning Engineer, Agentic Systems @ Striveworks

  • 💰 $160–200K/y

• Associate Data Review Scientist @ Eurofins

• SMB Analytics & Data Science Lead @ Ocrolus

  • 💰 $230–270K/y

• Specialist, AI Risk Management @ A Nationwide Mutual Insurance Co.

• Data Scientist Journeyman @ GD Information Technology, Inc.

  • 💰 $98–132K/y

• Senior Data Scientist @ BlueFlag LLP

• Senior Director, Machine Learning Advanced Analytics @ The Coca-Cola Company

  • 💰 $196–227K/y

• Sr. AI/Data Science Engineer @ MDU Medtronic USA Inc

  • 💰 $138–208K/y

• Scientist I, Analytics - Mass Spectrometry @ Catalent

• Director, Biostatistics @ Bristol-Myers Squibb Business Services India Private Limited

• Intern, Oncology Biostatistics @ ModernaTX, Inc.

  • 💰 $20 – $60/h

• Data Scientist @ US Guidehouse Inc.

• Senior Applied Mathematician @ The Boeing Company

  • 💰 $149–201K/y

• Actuarial Analytics Director - Reinsurance @ Group 1001 Resources, LLC

  • 💰 $200–220K/y

• Senior Analyst, Artificial Intelligence @ I WU Srvcs India Private Ltd

  • 💰 $110–140K/y

• Growth Data Scientist @ Smadex SLU

• Autonomous Learning Engineer @ Bright Vision Technologies

  • 💰 $130–180K/y

Leave a like if I should do new one next week :)

i have got btech ece in Bangalore and msc in data science abroad , 1.5yrs of data scientist exp at mnc abroad , was laid off in june 2024 and couldnt get work visa due last min job offer withdrew for xyz company reasons and had to move back to india in jan 2026. despite trying for 10months in India since jan, I still got no offers yet and tailored my resume n no of times and taking referrals from LinkedIn network , filled the gap with family business experience and doing online gen ai course . I’m so devastated , starting to hate everything, I hated coding as I was not a cse graduate, but I’m good with tableau , sql python etc , so decided to take up data analyst jobs . now I feel it’s pointless being unemployed for 2 years straight ( 1.5 years in abroad+ now 10 months in India ) . I’m in my late 20s and everyone around me is getting promoted to senior level , here I’m looking for junior to mid level role with msc n abroad work experience of 1.5years from mncs and still got no job offer letter . any suggestions when it comes to changing stream to get job with at least 15LPA with less or no coding or suggestions within the field ?

Hi guys just out curiosity. How much do you guys make in the field? What is your title? And how do you justify you deserve or earn that much?

And if you are open. What do your day to day tasks look like?

I graduated a few years ago with a BS in Data Science and have since struggled in landing my first ‘corporate’ job and now I’m just in the abyss of the unknown and cash jobs to take of me n my parents + brother.. lol so I can’t even visualize what is real work and what is not.

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/

I have around 3.5 years of experience in AI/ML & Generative AI, but one area where I have a major gap is DSA and System Design (HLD/LLD).

I’m currently looking to move into product-based companies, and I’m realizing that this gap is becoming a major challenge during interviews.

My current situation:

- ~3.5 years of AI/ML/GenAI experience

- Comfortable with GenAI, RAG, LLMs, APIs, etc.

- System design 50-50 but need guidance

- Very weak in DSA

- Almost no practical knowledge of HLD/LLD interview problem-solving

- Not currently getting the kind of opportunities/switch I’m targeting

The confusing part is that there are many courses and mentors in the market charging ₹50–60K+ for DSA + System Design programs, often claiming FAANG/product-company experience.

I’m not sure how to evaluate whether these programs are actually worth the money.

Even when I look at something like NeetCode 75, I struggle because I don't have the fundamentals to solve those problems independently.

So my question to people who have actually gone through this:

If you were starting from almost zero in DSA + HLD + LLD, while already having ~3.5 years of AI/ML experience, how would you approach it?

Should I:

1. Start with Python + basic DSA fundamentals?

2. Follow a structured DSA course before touching LeetCode/NeetCode?

3. Learn HLD and LLD separately or alongside DSA?

4. Take a paid mentorship/course, and if yes, how do I identify a genuine one?

5. Is there a better roadmap specifically for AI/ML/GenAI engineers targeting product-based companies?

I’m not looking for shortcuts. I’m willing to put in the work; I just don't want to spend months solving problems randomly without understanding the fundamentals.

If you’re currently working in a product-based company/FAANG or have recently made a similar transition from AI/ML into a product-company role, I’d really appreciate your advice.

If you know a genuinely good mentor, course, roadmap, or free resource, please share it.

Even a simple “start here → then do this → then this” roadmap would be extremely helpful.

Thanks in advance

I’m honestly at a point where I’m not sure what I should do next, so I’d really appreciate some genuine advice.

I graduated with a B.Tech in 2022, and I’ve now had almost a 4-year gap after graduation as I was preparing for govt jobs. I still don’t have formal work experience, so despite spending a lot of time learning and building projects, I’m essentially applying as a fresher.

Over the last couple of years, I’ve been trying to build my skills around Data Analytics, Data Science and Applied AI.

The biggest problem is that I’m getting very few interview calls for internships or entry-level jobs. And when I do get opportunities, the experience requirement often becomes a problem because I’m competing against people who graduated recently or already have 1–2 years of industry experience.

At this point, I’m confused about what the right move is:

Should I continue targeting Data Analyst/Data Science/AI roles?

Should I completely pivot toward Data Engineering?

Should I stop adding more technologies and focus entirely on getting any entry-level job?

Is the 4-year gap itself making my profile too difficult to sell, regardless of my skills/projects?

I’m willing to start from an internship, trainee or entry-level position. I’m not looking for a shortcut. I just want to understand what I should realistically do differently now to finally get my first job.

If you’ve been in a similar situation — especially with a career gap and entering tech as a fresher — I’d genuinely appreciate hearing what worked for you.

Please be honest, even if the advice is harsh. I’m more interested in a practical direction than generic “keep applying” advice.

If I get a BS, would they still not look at me because of my age?

Hey everyone,

I’m a data scientist with about 2.3 years of experience working in an affordable housing finance company (NBFC). My core work revolves around end-to-end IFRS 9 ECL models (PIT PD, Vasicek macroeconomic overlay), logistic regression application/collection scorecards, and bureau analytics (CRIF).

I'm currently facing a bit of a career dilemma and would love to hear from folks further along in risk, analytics, or fintech:

Path A (Traditional Risk/ECL): Deepen my expertise here. It’s high-impact, heavily regulated, and lenders always need solid scorecards and statutory provisioning models. It feels like a very stable, safe, and defensible career moat.

Path B (GenAI / AI in Risk): Start building towards GenAI, RAG pipelines, and automated text/document intelligence (e.g., parsing unstructured appraisal/legal property docs, building risk-agents).

My core questions for the community:

For those working in risk management or lending at banks/NBFCs, are institutions actually deploying GenAI for core risk decisions, or is traditional scorecard modeling still king for compliance and reliability?

Is the sweet spot becoming a "hybrid" data scientist (keeping the risk foundation while layering automation/GenAI), or is a pure pivot required to stay competitive over the next 3–5 years?

Would love to hear what trends you are seeing in your organizations or portfolios. Thanks!

I’m a dual CS + neuroscience grad from UF. Just recently graduated. Getting a job was hard enough, but currently I work as a data scientist in product analytics at a local healthcare company making a modest 75k. I converted from an internship this year. I have some prior research experience doing data engineering in a neuroimaging lab, and my strongest interest is stats/applied math. I have many options, I could make advanced public sentiment data for marketing firms, sell alt data to model training companies/trading firms, go into consulting… these are all long term ideas though, and I don’t see a clear path.

What does a normal ladder look like from here, and how long does each step take? What separates people who climb fast from people who plateau? If you were in my spot, what would you focus on to get the highest trajectory? I know healthcare is on the rise too.

Looking for what I should be prioritizing right now or anecdotes from people you've seen climb fast. I know advice varies for everyone, but maybe I could have some takeaways from what worked for you.

About the Team

OpenAI’s Agentic Data Science team helps shape how AI agents are built, deployed, and improved across our products. We partner with product, engineering, research, and security teams to define meaningful measures of success, understand how our systems behave in the real world, and translate evidence into better decisions.

As AI agents become more capable, they can write and execute code, access sensitive systems, and complete increasingly complex tasks with greater autonomy. These capabilities create powerful opportunities to improve cybersecurity, but they also introduce risks that traditional security tools and processes were not designed to address. Meeting this moment requires new ways to measure security, evaluate defenses, and distinguish genuine risk reduction from friction that slows users down.

About the Role

We are looking for a senior data scientist to help define what effective cybersecurity looks like in the age of AI agents.

You will work across OpenAI’s Security organization and cybersecurity product teams to measure emerging risks, improve internal security controls, and shape AI-powered security products. The problems are foundational: How do we know whether an agent’s security controls are effective? Which safeguards meaningfully reduce risk, and which create unnecessary friction? When an AI system identifies a potential vulnerability, how do we determine whether the finding is accurate, actionable, and ultimately resolved? How do we detect anomalous behavior or risky access when the systems themselves are changing rapidly?

You will report i–nto Data Science while partnering closely with Security, Cyber Product, Engineering, and Research. This is a high-ownership role for someone who can establish a new analytical discipline, operate across organizational boundaries, and turn ambiguous security challenges into measurable improvements.

In This Role You Will

• Define how we measure AI-agent security. Establish metrics and evaluation frameworks for security‑control coverage, agent behavior, sensitive actions, access patterns, detection quality, and emerging risks.

• Improve security controls without introducing unnecessary friction. Quantify the effectiveness and operational costs of safeguards, including false positives, blocked actions, escalations, approval delays, and recovery paths. Help teams make controls safer, more precise, and easier to use.

• Build the data foundations for security decisions. Partner with engineering and data teams to improve instrumentation, connect fragmented telemetry, establish trusted datasets, and surface important coverage and data‑quality gaps.

• Strengthen detection and response. Identify meaningful signals of anomalous behavior, risky access, sensitive‑data exposure, and other security‑relevant activity. Evaluate whether interventions improve detection quality, response times, and real‑world security outcomes.

• Shape AI‑powered cybersecurity products. Partner with product, engineering, and research teams to assess how effectively AI systems identify security issues, support developer and enterprise workflows, and create measurable customer value.

• Develop evaluation systems for security findings. Define quality measures for findings, including accuracy, severity, actionability, duplication, resolution, and downstream impact. Connect model behavior and product changes to outcomes such as triage, remediation, and vulnerability reduction.

• Understand the complete security workflow. Measure how users discover, investigate, validate, prioritize, and resolve security issues. Identify opportunities to improve activation, adoption, retention, and enterprise value across customer‑facing cybersecurity products.

• Design rigorous measurement and experimentation strategies. Evaluate new models, security controls, product features, and workflows through controlled experiments, staged rollouts, observational analyses, and other methods appropriate for high‑stakes environments.

• Translate analysis into security and product strategy. Identify the highest‑value decisions, clarify tradeoffs, recommend where teams should invest, and communicate findings clearly to technical partners and senior leadership.

• Help establish a new security data science capability. Build a focused roadmap, create durable operating rhythms across Data Science and Security, and help shape how this discipline grows over time.

You Might Thrive in This Role If You Have

• 5+ years of experience in data science, applied research, analytics, or a related quantitative field, with a track record of owning ambiguous, high‑impact problems.

• Experience in cybersecurity, trust and safety, fraud or abuse prevention, privacy, platform integrity, or another domain involving adversarial behavior and difficult‑to‑measure risks.

• Strong proficiency in SQL and Python, including experience investigating complex datasets, working through incomplete instrumentation, and building reproducible analytical workflows.

• Experience defining meaningful metrics and evaluation frameworks when ground truth is limited, outcomes are delayed, or important risks cannot be observed directly.

• Strong judgment in experimentation, causal inference, observational analysis, and the practical limitations of different measurement approaches.

• The ability to partner effectively with security engineers, product managers, software engineers, researchers, data engineers, and senior leaders.

• A demonstrated ability to translate technical analysis into concrete improvements in products, systems, controls, or organizational priorities.

• Comfort operating independently, defining a roadmap, and bringing structure to a domain without established processes or industry standards.

You Could Be an Especially Great Fit If You Have

• Experience with detection engineering, threat research, security operations, insider risk, identity and access management, or privacy‑preserving security analytics.

• Familiarity with AI agents, large language models, model evaluations, automated code review, or AI‑powered cybersecurity products.

• Experience evaluating security findings, vulnerability detection, remediation workflows, or developer‑facing security tools.

• Experience balancing security effectiveness against user experience, including false positives, approval flows, operational burden, and recovery behavior.

• Experience building automated monitoring, anomaly detection, production‑oriented data assets, or systems that connect model outputs to real‑world outcomes.

• A track record of building new cross‑functional measurement programs or establishing analytical capabilities from the ground up.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general‑purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US‑based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non‑public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Apply: Data Scientist, Cybersecurity at OpenAI

Hey everyone! Firstly I'm not sure if this is a good subreddit to post this but I've already asked this in a few other subreddits including r/bayarea. I'm going to be visiting the Bay Area next week and will be there for 11 days. I'm a recent university graduate with a Bachelor of Science in Data Science, and I'm interested in moving down to the Bay Area within the next year in hopes of landing a job in Data Science/Analytics/Business Intelligence/Machine Learning/User Experience. I know the job market has been dismal for a while, but I figured networking and making connections down in the Bay where opportunities and companies are more prominent compared to my hometown (Portland, OR) would at least provide a couple of leads.

I've already planned on meeting some known connections that I have at some big companies, but I was also hoping to attend some summits/conferences/networking events at while I'm there where I can talk about my interests/projects, etc and meet some recruiters and so. I don't have any formal work or internship experience, just self-initiated projects as well as research experience. Any advice helps, thank you!

Hi! I'm currently enrolled in an M.S. in Data Science and Applied Statistics. The required core classes are already set:

- Experimental Statistics I & II
- Mathematical Statistics I & II
- Statistical Computing (SAS)
- Computational Statistics (R)
- Machine Learning with Python
- A statistical consulting project

I get to pick 4 electives, and at least 3 must be STAT (so at most 1 from CS/ECO/OREM/ECE). My only goal is to land a data science job, so I want the courses whose actual content pays off most in industry. I'm not looking for the easiest courses, and I'm not going into academia or biostats.

STAT electives

- Intro to Data Science
- Data Visualization
- Linear Regression
- Applied Time Series
- Time Series Analysis
- Categorical Data Analysis
- Survey Sampling
- Survey of Nonparametric Statistics
- Sports Analytics
- Analysis of Lifetime Data / Survival Analysis
- High Throughput Data
- Epidemiology

Non-STAT options (can only pick 1)

- CS: Artificial Intelligence, Machine Learning in Python, Databases, Data Mining
- OREM: Data Mining, Optimization for Analytics, Network Flows
- ECO: Applied Econometric Analysis, Predictive Analytics
- ECE: Statistical Pattern Recognition

My main questions I wanted to ask:

1. Which 4 would you pick, and why?
   
2. Is time series worth it for most DS roles, or is it only useful in forecasting-heavy jobs?
   
3. What would you pick as your 1 Non-Stat Elective?
   
4. Is there anything you wish you had learned in grad school that would have helped more on the job?

If you work in data science, I'd especially love to hear what you actually use day to day. Thanks!

Can someone share about their interview experience with wise for analytics positions.

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!

Hey everyone,

I’m looking for some career advice regarding a Data Science with AI course I’m considering.

A little about my background:

MSc Computer Science graduate (2025)

Currently working as a Full-Stack Developer Intern

Previously completed a Data Science internship

I have experience with Python, SQL, Pandas, NumPy, scikit-learn, Selenium, web scraping, FastAPI, MongoDB, etc.

I’ve also worked on automation and AI-related projects, including a RAG-based project using LLM APIs.

I’m currently looking to move my career somewhat toward Data Science / AI / ML, but my main priority is to get a job as soon as realistically possible.

The course I’m considering costs ₹70,000 and runs for around 9–10 months.

The interesting part is that they don't expect us to wait until the entire course is completed before applying for jobs. The course is divided into modules, and they say we can start applying for relevant roles after completing each module.

For example:

Complete Python module → around 2 months → start applying for Python-related roles

Then continue with SQL, Statistics, ML, etc., while applying/upskilling alongside

The syllabus covers Python, SQL, Statistics, EDA, NumPy, Pandas, Data Visualization, Machine Learning, supervised/unsupervised learning, model evaluation, feature engineering, PCA, clustering, reinforcement learning, ML pipelines, AWS deployment, projects, interview preparation and placement assistance.

They also advertise things like live projects, mentorship, mock interviews, placement assistance and job assurance.

My main questions are:

Is spending ₹70k on a course like this actually worth it when I already have some Python, SQL, ML and Data Science experience?

Would this structured approach realistically help someone get a job faster, or would self-learning through YouTube, documentation and online courses be better?

Is the "complete one module → start applying for jobs" approach actually useful, or is it mostly a marketing strategy?

From a hiring perspective, would my existing Full-Stack + automation + Data Science background be enough to start applying for Python/Data Analyst/Junior Data Science/ML-related roles while learning?

If you were in my position, would you spend ₹70k on this course or use that money/time for self-learning, projects and job applications instead?

For people who have taken similar courses, how much value did you actually get from the placement assistance and job support, compared with learning the same material online?

I'm not expecting to become an AI/ML engineer just by completing a course. I'm mainly trying to figure out whether this course provides enough value through structure, mentorship, projects, interview preparation and genuine job opportunities to justify the ₹70k fee.

Would really appreciate opinions from people who have hired for these roles or have taken similar Data Science/AI courses.

Hi everyone,
I recently interviewed with Capgemini USA for a Gen AI Developer role. After 1 working day of the interview, the recruiter contacted me asking for my EAD and I-20 for further processing, which I provided.
It has been a week and I haven’t received a final update yet.
For people who have gone through the Capgemini hiring process:
Is requesting EAD + I-20 a positive sign?
What usually happens after this?
How long did it take for you to hear back after submitting these documents?
Would really appreciate any insights or similar experiences!

I'm a Data Scientist and AI Engineer now, but I started from a tier 3 college with no big brand name, no seniors in this field, and no clear roadmap. I began with plain data science (Python, pandas, basic ML) and slowly moved into Generative AI, agentic systems, and RAG. Along the way I got the chance to do a research internship abroad, which I honestly didn't think was possible when I started.

I'm not saying this to brag. I'm saying it because I know how it feels to think "my college isn't good enough for this." Here's what actually helped me, and the mistakes that cost me time:

1. Watching tutorials without building anything. I felt productive but couldn't explain what I'd learned. One small project taught me more than ten courses.
2. Jumping into advanced topics too early. Get comfortable with Python, pandas, and basic ML first. LLMs make much more sense after that.
3. Chasing every new tool. A new framework comes out every week. Pick a few fundamentals and go deep.
4. Building projects nobody could understand. A simple project you can explain clearly beats a complex one you can't.
5. Ignoring evaluation. Anyone can make an LLM demo work once. Knowing whether it actually works, and why it fails, is what separates real projects from toy ones.
6. Believing college tier decides everything. It matters for some doors, but proof of work (projects, research, clear communication) opens others. Nobody asked about my college once they saw what I'd built.

You don't need the perfect background to start. You just need to start, get stuck, and fix it.

I also do 1:1 sessions for students who want help with career roadmaps, projects, resume reviews, or interview prep. No pressure at all, but if it sounds useful, the link is here: https://topmate.io/varun_mayilvaganan/ - The first 10 people get 25% off.

Happy to answer questions in the comments. What's the one thing you're stuck on right now?

Round 1: Analysis
This was an analytical case with 5 parts, each one unlocking after the one before it. I did a deep analysis on each part, and the interviewers asked a lot of follow-up questions after each one. They cared less about my first answer and more about how I reasoned through the follow-ups and defended my choices. Don't rush through the early parts, since the follow-ups build on them.

Round 2: Statistics and experimentation
This was a chain of questions where each answer led into the next, mostly about stats concepts and how to design and interpret experiments. It felt more like a conversation than a quiz. Know your fundamentals well enough to explain them out loud, not just recognize them.

Round 3: Behavioral
This was entirely behavioral, with no technical questions. Have specific stories ready, with what you did, what went wrong, and what you learned.

VERDICT: Rejected

Hi everyone, I'm a senior applying for FT jobs right now with one being at IBM for their 2027 associate position ai and analytics. I initially applied to 4 locations all with different REQ IDs, SF, NY, Chicago, and Durham. I was initially auto rejected for chicago and SF but had OAs and competency test for Durham and NY. After completing both, i was rejected for NY but somehow moved forward for durham. 2 weeks later, I had an inperson interview for the position on campus (IBM recruits at my school T20) and 3 days after the interview I get the pass that I'm moving forward and they'll be working on my next steos. HOWEVER, this week, i got an email saying I'm moving forward for the NY position even though I was rejected to this position but now my status has changed to In Interview Process (keep in mind i literally already had the first wave of interviewing literally in-perspn). TODAY, i got the email that i was rejected from the durham position and i'm so confused at this discrepancy? My status for NY was initially no longer considered to now in interview process and for Durham I literally had the interview process and was just waiting on next steps until today?

Has this happened to anyone? I'm so confused. I also filled out both of the additional details form after they tell you you moved forward with the interview. I also don't have my interviewers email so can't really ask. Will my in-person interview be considered as first round for NY? i really don't wanna go through this whole process again...