We’re looking for an experienced Data Scientist to support a pricing-strategy project.

📍 Berlin — 2 days per week in the office

⏳ 3–4-month contract

📅 Start: ASAP

🤝 Direct contract with the client

You’ll need strong Python and SQL skills, experience building predictive models and simulations, and the ability to explain complex insights clearly. Experience in fintech, insurance, pricing, or other complex systems would be ideal.
We’re looking for someone highly analytical, commercially minded, and a great communicator.

Interested anyone know someone or share your updated resume

marat@zerotoonesearch.com

Two Scholarships Open Now — Airtel (UG Engineering) & IDFC FIRST Bank (MBA)
Two separate scholarship programs are currently accepting applications for Indian students.
Scholarship 1: Bharti Airtel Scholarship 2026-27
Who can apply:
First year UG or 5 year integrated engineering students at Top 50 NIRF ranked institutes
Fields: ECE, Telecom, IT, CS, Data Science, Aerospace, Emerging Tech
Indian citizen and resident
Family income under ₹8.5 lakh per annum
What you get: 100% tuition, hostel and mess fees, plus a free laptop (first year only, one time)
Deadline: July 31, 2026 Apply here: https://bhartiairtelfoundation.org/bharti-airtel-scholarship
Scholarship 2: IDFC FIRST Bank MBA Scholarship 2026-28
Who can apply:
First year students of a 2 year full time MBA/PGDM program at listed eligible colleges
Family income under ₹6 lakh per annum
Age 35 or below on date of application
Valid mobile number linked to Aadhaar
What you get: ₹2 lakh total over 2 years (₹1 lakh per year) toward MBA tuition fees only. Does not cover hostel, mess, books, or travel
Deadline: August 8, 2026 Apply here: https://www.idfcfirstbank.com/csr-activities/educational-initiatives/mba-scholarship
Note: IDFC FIRST Bank does not charge any application fee at any stage.

If you need more such type of content then do let me know by responding to this message. 😊

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Role: SWE and AI Internship
Batch: 2026/27 passouts

Link: https://www.lanmea.com/careers/ai-software-engineering-intern

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📊 Data Science Roadmap 🚀

📂 Start Here
∟📂 What is Data Science & Why It Matters?
∟📂 Roles (Data Analyst, Data Scientist, ML Engineer)
∟📂 Setting Up Environment (Python, Jupyter Notebook)

📂 Python for Data Science
∟📂 Python Basics (Variables, Loops, Functions)
∟📂 NumPy for Numerical Computing
∟📂 Pandas for Data Analysis

📂 Data Cleaning & Preparation
∟📂 Handling Missing Values
∟📂 Data Transformation
∟📂 Feature Engineering

📂 Exploratory Data Analysis (EDA)
∟📂 Descriptive Statistics
∟📂 Data Visualization (Matplotlib, Seaborn)
∟📂 Finding Patterns & Insights

📂 Statistics & Probability
∟📂 Mean, Median, Mode, Variance
∟📂 Probability Basics
∟📂 Hypothesis Testing

📂 Machine Learning Basics
∟📂 Supervised Learning (Regression, Classification)
∟📂 Unsupervised Learning (Clustering)
∟📂 Model Evaluation (Accuracy, Precision, Recall)

📂 Machine Learning Algorithms
∟📂 Linear Regression
∟📂 Decision Trees & Random Forest
∟📂 K-Means Clustering

📂 Model Building & Deployment
∟📂 Train-Test Split
∟📂 Cross Validation
∟📂 Deploy Models (Flask / FastAPI)

📂 Big Data & Tools
∟📂 SQL for Data Handling
∟📂 Introduction to Big Data (Hadoop, Spark)
∟📂 Version Control (Git & GitHub)

📂 Practice Projects
∟📌 House Price Prediction
∟📌 Customer Segmentation
∟📌 Sales Forecasting Model

📂 ✅ Move to Next Level
∟📂 Deep Learning (Neural Networks, TensorFlow, PyTorch)
∟📂 NLP (Text Analysis, Chatbots)
∟📂 MLOps & Model Optimization

Data Science Resources: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z

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Honeywell is hiring Data Scientist 🚀

Experience : 0-1 Year
Location : Bangalore

Apply link : https://www.linkedin.com/jobs/view/4445679023/

✅ Deep Learning Models You Should Know 🧠📚

1️⃣ Feedforward Neural Networks (FNN)
– Basic neural networks for structured/tabular data
– Example: Classification or regression on tabular datasets

2️⃣ Convolutional Neural Networks (CNN)
– Specialized for image and spatial data
– Example: Image classification, object detection

3️⃣ Recurrent Neural Networks (RNN)
– Processes sequential data
– Example: Time series forecasting, text generation

4️⃣ Long Short-Term Memory (LSTM)
– A type of RNN for long-range dependencies
– Example: Stock price prediction, language modeling

5️⃣ Gated Recurrent Unit (GRU)
– Lightweight alternative to LSTM
– Example: Real-time NLP applications

6️⃣ Autoencoders
– Unsupervised learning for feature extraction & denoising
– Example: Anomaly detection, noise reduction

7️⃣ Generative Adversarial Networks (GANs)
– Generates synthetic data by pitting two networks against each other
– Example: Deepfakes, art generation, image synthesis

8️⃣ Transformer Models
– State-of-the-art for NLP and beyond
– Example: Chatbots, translation (BERT, GPT)

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7 Steps of the Machine Learning Process

Data Collection: The process of extracting raw datasets for the machine learning task. This data can come from a variety of places, ranging from open-source online resources to paid crowdsourcing. The first step of the machine learning process is arguably the most important. If the data you collect is poor quality or irrelevant, then the model you train will be poor quality as well.

Data Processing and Preparation: Once you’ve gathered the relevant data, you need to process it and make sure that it is in a usable format for training a machine learning model. This includes handling missing data, dealing with outliers, etc.

Feature Engineering: Once you’ve collected and processed your dataset, you will likely need to transform some of the features (and sometimes even drop some features) in order to optimize how well a model can be trained on the data.

Model Selection: Based on the dataset, you will choose which model architecture to use. This is one of the main tasks of industry engineers. Rather than attempting to come up with a completely novel model architecture, most tasks can be thoroughly performed with an existing architecture (or combination of model architectures).

Model Training and Data Pipeline: After selecting the model architecture, you will create a data pipeline for training the model. This means creating a continuous stream of batched data observations to efficiently train the model. Since training can take a long time, you want your data pipeline to be as efficient as possible.

Model Validation: After training the model for a sufficient amount of time, you will need to validate the model’s performance on a held-out portion of the overall dataset. This data needs to come from the same underlying distribution as the training dataset, but needs to be different data that the model has not seen before.

Model Persistence: Finally, after training and validating the model’s performance, you need to be able to properly save the model weights and possibly push the model to production. This means setting up a process with which new users can easily use your pre-trained model to make predictions.

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