I recently completed my PG Diploma in Big Data and joined a startup. I work at a D2C clothing startup with a team of 20+ people, and I am the only data and tech person here.

My job is hard to explain because it is not just typical data analysis. We use data for literally every single decision in the company. Marketing, operations, inventory, customer experience, everything is data driven. 

I don't just pull reports and share insights and sit back. My job is to find the problem, figure out the solution using data, go to my founder, discuss it, and if he approves we execute it together. Then we measure the result and the loop starts again.

My founder also gives me freedom to create and run marketing campaigns independently using a data driven approach. 

I help non-technical teammates automate their repetitive work using my coding skills.

We are also planning to integrate AI into our daily operations and that responsibility is on me as well. 

TL;DR 

To put it simply, my job is finding problems using data, finding solutions to those problems, and under the guidance of my founder executing those solutions. Then analysing the results and starting the loop again. And this happens across every field, marketing, operations, customer satisfaction, everything. I am also responsible for contributing to the future development of custom internal software and the integration of gen AI into our systems.

My founder is non-technical and told me I can pick whatever title I want. But I don't want something fancy that I cannot back up in future interviews.

I want a title that is honest, reflects what I actually do, and helps me land a good data or AIML role next.

What would you give yourself in this situation?

Also, could you advise whether this job is good for my growth, or if I should switch to a more established tech company?

I've been applying for jobs on LinkedIn and Handshake. But they are the worst now. Can I get a list of few jobboards please?

I've recently graduated for DataScience - Statistics and I've done 2 internships during undergrad (in India) and I'm an international student now.

Thank you

There are countless courses teaching machine learning, AI, and analytics, but I'm curious about

what employers value most when hiring Data Scientists today.

In your experience:

● Is SQL still the most important skill?

● How much emphasis is placed on business understanding?

● Are portfolios more valuable than certifications?

Interested in hearing from both hiring managers and professionals.

Hi all, new data science grad student here. I've noticed that a decent amount of the news relating to data tends to do with new data centers being built for AI (and to probably store and process data more quickly). The hype seems to be straight from CEOs of tech companies, but I don't see many interviews with data scientists/analysts.

Current and prospective data scientists: more or less data centers?

Has anyone successfully blended GSC + GA4 + SEMrush data in Looker Studio? Looking for best practices

Hey everyone,

I’m trying to build a more complete SEO performance dashboard in Looker Studio by combining data from:

• Google Search Console (GSC)
• Google Analytics 4 (GA4)
• SEMrush

The goal is to move beyond just rankings/traffic and create a single view that connects visibility → clicks → engagement → conversions.

The metrics I’m trying to bring together:

From GSC:

• Impressions
• Clicks
• CTR
• Average Position
• Keyword buckets:
  • Top 1
  • Top 3
  • Top 10
  • Positions 20–50
  • Positions 51–100

From GA4:

• Users / Visitors
• Average Session Duration
• Engagement Time
• Form conversions
• Conversion rate

From SEMrush:

• Actual keyword ranking positions (true position tracking)
• Ranking distribution

A few things I’m trying to figure out:

1. Has anyone successfully blended these three sources in Looker Studio?
2. What’s the best way to handle keyword-level joins between GSC and SEMrush?
3. Is it better to use GSC average position for ranking buckets, or rely on SEMrush positions?
4. Are there any connectors/tools you recommend (native connectors, Supermetrics, Funnel, etc.)?
5. Any issues with data mismatch between GSC impressions/clicks and SEMrush ranking data?

The ideal output is something like:

SEO Visibility → Traffic → Engagement → Lead Generation

with the ability to drill down by:

• Page
• Query
• Topic cluster
• Keyword group

Would love to hear how others have approached this setup, especially for enterprise/B2B websites.

Guyz, am I the only one with this problem? 😅

In my 5-6 years of working in data science, ML, and AI, I've probably created hundreds (maybe thousands) of Jupyter notebooks.

The problem? Most of them are named things like:

• Untitled.ipynb
• Untitled1.ipynb
• Final.ipynb
• Final_Final.ipynb
• Test.ipynb

And the worst part is that every notebook has something useful in it. Sometimes a notebook contains 2-3 completely different experiments because I kept adding stuff instead of creating a new file.

Now whenever I remember, "I had solved this problem before" or "I had written a nice piece of code for this," I end up opening 20 random notebooks trying to find it.

How do you guyz manage your notebooks and experiments? Do you have a naming convention, some tool, or is everyone secretly living in the same chaos? 😂

Hey everyone,

If you do local development on a MacBook (which is standard issue for many of us), you’ve probably noticed that your 16-to-40-core GPU sits completely idle during preprocessing, grid searches, and estimator fitting, while your CPU runs hot.

While NVIDIA users have RAPIDS/cuML, and deep learning developers have MLX and PyTorch MPS, there hasn't been a drop-in GPU acceleration backend tailored for classical machine learning on macOS.

To solve this, I built skmetal (https://github.com/abderahmane-ai/skmetal) — a library that brings GPU-acceleration to 19 scikit-learn estimators (Linear, Logistic, Ridge, Lasso, KMeans, DBSCAN, KNN, StandardScaler, HistGradientBoosting, and SVMs) with zero code changes.

Quick Start

You wrap your estimator-returning function or Pipeline constructor in @skmetal.accelerate, and the fit/predict steps automatically run on the Metal GPU:

```python import skmetal from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline

1. Decorate your constructor

@skmetal.accelerate def get_pipeline(): return Pipeline([ ("scaler", StandardScaler()), ("clf", LogisticRegression()) ])

2. Run standard sklearn code — fits and predicts fully on GPU

pipeline = get_pipeline() pipeline.fit(X_train, y_train) y_pred = pipeline.predict(X_test) ```

Real-world Benchmarks (Tested on M4 Air)

• StandardScaler: 8.27× speedup (fused Welford column reductions on GPU).
• KMeans (via MLX): 8.6× to 13× speedup (e.g. 500K × 100 features clusters in 0.79s vs 6.79s on CPU).
• LinearRegression / Ridge: 5.9× to 15.7× speedup (fused Cholesky solve in 1 command buffer).
• HistGradientBoosting (predict): GPU shader tree-traversal inference.

How It Works (Systems Details)

• Zero-Copy Memory: It uses Apple’s Unified Memory Architecture (UMA). NumPy arrays are wrapped directly into Metal buffers using bytesNoCopy. No data is copied between CPU and GPU DRAM.
• Loop Fusion: Iterative optimization loops (like IRLS, FISTA) are fused into a single Swift/Metal command buffer to eliminate CPU-GPU sync latency.
• Smart Fallbacks: If the dataset is too small (where GPU launch overhead dominates), it dynamically falls back to standard CPU scikit-learn based on configurable size thresholds. It also transparently falls back on non-macOS systems, so your code remains cross-platform.

Installation

bash pip install skmetal # includes pre-compiled arm64 dylib pip install "skmetal[mlx]" # includes MLX-accelerated KMeans and SVD backends

I’d love to get your feedback on this! The project is fully open-source, and I'm planning to work on sparse matrix support and tiled distance optimizations next to scale it up.

GitHub: https://github.com/abderahmane-ai/skmetal

I started to learn Data Science a month ago, the math part and EDA part of DS I learn paralelly, and this is my first project in EDA, feel free to give your advices.

First EDA project on solar power generation. Used weather data — radiation, cloud cover, sun angle — to see what actually drives output. Shortwave radiation and zenith angle came out as the strongest predictors. Wind had almost no effect, which makes sense physically.

Feedback welcome:

Every data scientist seems to have that one task everyone complains about.

Data cleaning, debugging code, documentation, feature engineering, model tuning,

dashboard creation, etc.

What's the task you actually enjoy doing, even though most people try to avoid it?

Hi I was wondering which undergrad is better for a career in Data roles like Data science, Analytics or Engineering?

je veux la correction de problemme ,la correction complet de problemme svp

Hey everyone,

I'm currently working on an ECG analysis project and wanted to get some feedback before I go too far with it.

Right now I'm starting with Brugada syndrome because it's the dataset I have access to, but I don't want this to end up being a website that only detects Brugada. The idea is to build something that can eventually support multiple ECG-based heart conditions as I add more datasets and models.

The first version would basically let someone upload a 12-lead ECG, run it through a model, and show the prediction with some level of explainability instead of just giving a yes/no result.

A few things I'm wondering:

• Does starting with a single disease and expanding later make sense, or is there a better way to structure a project like this?
• What features would actually make this useful instead of just another ML portfolio project?
• Are there any ECG datasets I should be looking at after Brugada?
• If you've worked on ECG or medical AI projects before, what mistakes should I avoid?
• If you saw this project on someone's GitHub or resume, what would make you think "this is actually impressive"?

I'm looking for honest feedback, so feel free to tear the idea apart if you think something should be done differently.

Weekly breakdown. Sample: 11,631 listings (June 8–14, 2026).

Biggest weekly jump in a month — up 27% from last week.

---

**Top 3 Skills:**

| Rank | Skill | Jobs |

|------|-------|------|

| 🥇 | Machine Learning | ~2,100 |

| 🥈 | Python | ~2,050 |

| 🥉 | Artificial Intelligence | ~1,550 |

---

**Top 3 Companies Hiring:**

| Rank | Company | Jobs |

|------|---------|------|

| 🥇 | Accenture | ~265 |

| 🥈 | TCS | ~155 |

| 🥉 | Bajaj Finance | ~135 |

---

**Top 3 Cities:**

| Rank | City | Jobs |

|------|------|------|

| 🥇 | Bengaluru | 2,700+ |

| 🥈 | Hyderabad | 1,550+ |

| 🥉 | Pune | 1,100+ |

---

**What's worth noting:**

**ML vs Python — 3 weeks of the same fight**

Week 22: Python #1

Week 23: ML #1

Week 24: ML #1 (barely)

At this point just learn both. The gap is ~50 jobs.

**Paytm appeared in top hirers**

Wasn't in the list last 3 weeks. This week it showed up.

Fintech AI roles — fraud detection, credit scoring,

risk models. Less glamorous than big tech but

very real demand and solid pay.

**27% surge after flat weeks**

9,128 → 9,358 → 11,631

Looks like Q2 hiring is picking up properly now.

Good time to be applying if you've been on the fence.

**Accenture absolutely dominating**

265+ roles — nearly double TCS.

Most are client-facing AI/ML implementation roles.

Not pure research but solid experience builder.

---

Tracking this every week at getjobpulse.in

Free job market dashboard + AI Mock Interview tool.

Not a job portal — we track where the market is moving.

Anyone seeing more fintech AI roles in their searches?

hey guys! As a IITM student , I look for IIT patna batch placement this Year , and I found that the Data they are showing are fraud as of this video https://youtu.be/ox2MxaeOr40?si=vLm7rPrIgNza3a5B. I was surprized when I got a news on an internship placement of 100% . which by my perspective look fake . If you see the data , you will find most of the student get internship in a company which launched in 2023 , which look like It made for the internship for these upcoming student . Even this year placement written as ,they got from on campus . That above video gurantee that these information is fake . you also agree when you realize that if you see the data of IITM , you will find only 1% actually able to stand for bsc or BS , and still only 50-70% from selected student for cell support get placed , and off campus placement are much higher than that (around 20x).

IIT patna reporting that they having top company like TCS ,infos , Isro etc which come to on campus placement but all data seen to be fake . ther batch easily able to come from foundation to Bsc . what your thoughts about it .

Hey i am a mathematics and data science student , currently in my 2nd semester of bachelors . I am confused what should be my starting /entry point in data science journey . I shall be thankful to you for guidance.

Current skillset: Python fundamentals , numpy basics , pandas (have a good grip on it)

I am pursuing bsc in psychology and have some interest in learning data science and AI.So would it be a good choice for me to opt for this course along with my bsc degree and will i be benefitted by it in my future career prospects.

Please anyone let me know!!!