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EXL is looking for a Senior Neo4j Developer to join our growing data engineering team!

๐Ÿง  Experience Required:
โœ”๏ธ 10+ years overall in software/data engineering
โœ”๏ธ 4+ years of hands-on experience with Neo4j
โœ”๏ธ Strong background in Python and PySpark
โœ”๏ธ Experience in graph modeling, Cypher queries, and big data pipelines

๐ŸŒ Location: Open to all EXL locations [Hybrid]

Join us to build cutting-edge graph-based solutions that solve real-world business problems.

๐Ÿ“ฉ Interested or know someone who might be a great fit? Letโ€™s connect!
Share your resume at Qareena.Kazi@exlservice.com
โค1
Forwarded from Python for Data Analysts
๐Ÿณ ๐— ๐˜‚๐˜€๐˜-๐—›๐—ฎ๐˜ƒ๐—ฒ ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฎ๐—ป๐—ฑ ๐—ฎ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜

Want to land a career in data analytics? ๐Ÿ“Š๐Ÿ’ฅ

Itโ€™s not about stacking degrees anymoreโ€”itโ€™s about mastering in-demand skills that make you stand out in a competitive job market๐Ÿง‘โ€๐Ÿ’ป๐Ÿ“Œ

๐‹๐ข๐ง๐ค๐Ÿ‘‡:-

http://pdlink.in/3Uxh5TR

Start small, practice every day, and add these skills to your portfolioโœ…๏ธ
Machine learning powers so many things around us โ€“ from recommendation systems to self-driving cars!

But understanding the different types of algorithms can be tricky.

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

๐Ÿ. ๐’๐ฎ๐ฉ๐ž๐ซ๐ฏ๐ข๐ฌ๐ž๐ ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐ 
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

๐’๐จ๐ฆ๐ž ๐œ๐จ๐ฆ๐ฆ๐จ๐ง ๐ฌ๐ฎ๐ฉ๐ž๐ซ๐ฏ๐ข๐ฌ๐ž๐ ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐š๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐œ๐ฅ๐ฎ๐๐ž:

โžก๏ธ Linear Regression โ€“ For predicting continuous values, like house prices.
โžก๏ธ Logistic Regression โ€“ For predicting categories, like spam or not spam.
โžก๏ธ Decision Trees โ€“ For making decisions in a step-by-step way.
โžก๏ธ K-Nearest Neighbors (KNN) โ€“ For finding similar data points.
โžก๏ธ Random Forests โ€“ A collection of decision trees for better accuracy.
โžก๏ธ Neural Networks โ€“ The foundation of deep learning, mimicking the human brain.

๐Ÿ. ๐”๐ง๐ฌ๐ฎ๐ฉ๐ž๐ซ๐ฏ๐ข๐ฌ๐ž๐ ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐ 
With unsupervised learning, the model explores patterns in data that doesnโ€™t have any labels. It finds hidden structures or groupings.

๐’๐จ๐ฆ๐ž ๐ฉ๐จ๐ฉ๐ฎ๐ฅ๐š๐ซ ๐ฎ๐ง๐ฌ๐ฎ๐ฉ๐ž๐ซ๐ฏ๐ข๐ฌ๐ž๐ ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐š๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐œ๐ฅ๐ฎ๐๐ž:

โžก๏ธ K-Means Clustering โ€“ For grouping data into clusters.
โžก๏ธ Hierarchical Clustering โ€“ For building a tree of clusters.
โžก๏ธ Principal Component Analysis (PCA) โ€“ For reducing data to its most important parts.
โžก๏ธ Autoencoders โ€“ For finding simpler representations of data.

๐Ÿ‘. ๐’๐ž๐ฆ๐ข-๐’๐ฎ๐ฉ๐ž๐ซ๐ฏ๐ข๐ฌ๐ž๐ ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐ 
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

๐‚๐จ๐ฆ๐ฆ๐จ๐ง ๐ฌ๐ž๐ฆ๐ข-๐ฌ๐ฎ๐ฉ๐ž๐ซ๐ฏ๐ข๐ฌ๐ž๐ ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐š๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐œ๐ฅ๐ฎ๐๐ž:

โžก๏ธ Label Propagation โ€“ For spreading labels through connected data points.
โžก๏ธ Semi-Supervised SVM โ€“ For combining labeled and unlabeled data.
โžก๏ธ Graph-Based Methods โ€“ For using graph structures to improve learning.

๐Ÿ’. ๐‘๐ž๐ข๐ง๐Ÿ๐จ๐ซ๐œ๐ž๐ฆ๐ž๐ง๐ญ ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐ 
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

๐๐จ๐ฉ๐ฎ๐ฅ๐š๐ซ ๐ซ๐ž๐ข๐ง๐Ÿ๐จ๐ซ๐œ๐ž๐ฆ๐ž๐ง๐ญ ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐š๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐œ๐ฅ๐ฎ๐๐ž:

โžก๏ธ Q-Learning โ€“ For learning the best actions over time.
โžก๏ธ Deep Q-Networks (DQN) โ€“ Combining Q-learning with deep learning.
โžก๏ธ Policy Gradient Methods โ€“ For learning policies directly.
โžก๏ธ Proximal Policy Optimization (PPO) โ€“ For stable and effective learning.

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
Data Scientist โ€“ Fraud Risk๐Ÿš€

๐Ÿ“ Hyderabad | Gurgaon | Bangalore

Do you have a passion for fighting fraud with data & machine learning? ๐Ÿ’ก

Weโ€™re looking for Data Scientists / Sr. Data Scientists who love solving complex problems and want to make an impact in the world of Fraud Risk & Analytics.

โœจ What Youโ€™ll Work On
๐Ÿ”น Build & deploy advanced ML models to detect and prevent Payment Fraud
๐Ÿ”น Dive deep into SQL + Python + PySpark to analyze large datasets
๐Ÿ”น Spot hidden fraud patterns & create smarter prevention strategies
๐Ÿ”น Collaborate with cross-functional teams to continuously improve detection systems

๐Ÿ‘ฉโ€๐Ÿ’ป What Weโ€™re Looking For
โœ”๏ธ 2.5โ€“5 yearsโ€™ experience in SQL + ML (Classification & Regression Models)
โœ”๏ธ Strong skills in Excel, SQL, PySpark & Python
โœ”๏ธ Hands-on experience in fraud detection models (a big plus!)
โœ”๏ธ Immediate joiners (or <30 daysโ€™ notice) ONLY

๐Ÿ“ฉ Ready to fight fraud with us?
Share your resume at anupama.rao@straive.com
โค1
Step-by-Step Roadmap to Learn Data Science in 2025:

Step 1: Understand the Role
A data scientist in 2025 is expected to:

Analyze data to extract insights

Build predictive models using ML

Communicate findings to stakeholders

Work with large datasets in cloud environments


Step 2: Master the Prerequisite Skills

A. Programming

Learn Python (must-have): Focus on pandas, numpy, matplotlib, seaborn, scikit-learn

R (optional but helpful for statistical analysis)

SQL: Strong command over data extraction and transformation


B. Math & Stats

Probability, Descriptive & Inferential Statistics

Linear Algebra & Calculus (only what's necessary for ML)

Hypothesis testing


Step 3: Learn Data Handling

Data Cleaning, Preprocessing

Exploratory Data Analysis (EDA)

Feature Engineering

Tools: Python (pandas), Excel, SQL


Step 4: Master Machine Learning

Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost

Unsupervised Learning: K-Means, Hierarchical Clustering, PCA

Deep Learning (optional): Use TensorFlow or PyTorch

Evaluation Metrics: Accuracy, AUC, Confusion Matrix, RMSE


Step 5: Learn Data Visualization & Storytelling

Python (matplotlib, seaborn, plotly)

Power BI / Tableau

Communicating insights clearly is as important as modeling


Step 6: Use Real Datasets & Projects

Work on projects using Kaggle, UCI, or public APIs

Examples:

Customer churn prediction

Sales forecasting

Sentiment analysis

Fraud detection



Step 7: Understand Cloud & MLOps (2025+ Skills)

Cloud: AWS (S3, EC2, SageMaker), GCP, or Azure

MLOps: Model deployment (Flask, FastAPI), CI/CD for ML, Docker basics


Step 8: Build Portfolio & Resume

Create GitHub repos with well-documented code

Post projects and blogs on Medium or LinkedIn

Prepare a data science-specific resume


Step 9: Apply Smartly

Focus on job roles like: Data Scientist, ML Engineer, Data Analyst โ†’ DS

Use platforms like LinkedIn, Glassdoor, Hirect, AngelList, etc.

Practice data science interviews: case studies, ML concepts, SQL + Python coding


Step 10: Keep Learning & Updating

Follow top newsletters: Data Elixir, Towards Data Science

Read papers (arXiv, Google Scholar) on trending topics: LLMs, AutoML, Explainable AI

Upskill with certifications (Google Data Cert, Coursera, DataCamp, Udemy)

Free Resources to learn Data Science

Kaggle Courses: https://www.kaggle.com/learn

CS50 AI by Harvard: https://cs50.harvard.edu/ai/

Fast.ai: https://course.fast.ai/

Google ML Crash Course: https://developers.google.com/machine-learning/crash-course

Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998

Data Science Books: https://t.me/datalemur

React โค๏ธ for more
โค5๐Ÿ‘1
๐Ÿš€๐Ÿ”ฅ ๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ๐—ป ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—•๐˜‚๐—ถ๐—น๐—ฑ๐—ฒ๐—ฟ โ€” ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ
Master the most in-demand AI skill in todayโ€™s job market: building autonomous AI systems.

In Ready Tensorโ€™s free, project-first program, youโ€™ll create three portfolio-ready projects using ๐—Ÿ๐—ฎ๐—ป๐—ด๐—–๐—ต๐—ฎ๐—ถ๐—ป, ๐—Ÿ๐—ฎ๐—ป๐—ด๐—š๐—ฟ๐—ฎ๐—ฝ๐—ต, and vector databases โ€” and deploy production-ready agents that employers will notice.

Includes guided lectures, videos, and code.
๐—™๐—ฟ๐—ฒ๐—ฒ. ๐—ฆ๐—ฒ๐—น๐—ณ-๐—ฝ๐—ฎ๐—ฐ๐—ฒ๐—ฑ. ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ-๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ถ๐—ป๐—ด.

๐Ÿ‘‰ Apply now: https://go.readytensor.ai/cert-610-agentic-ai-certification

React โค๏ธ for more free resources
โค1
We're Hiring - Computer Vision Engineers & Al
Connect
We're expanding our team and looking for skilled
professionals to join us in building intelligent,
real-world solutions
Requirements:
2+ years of hands-on experience in Al/ML or
Computer Vision roles
Strong proficiency in Python
Solid experience with:
Open Cv
Pytourch
Image Classification
YOLO and object detection
Transfer Learning
Machine Learning frameworks and pipelines
Location: Mumbai (On-site)
Working Days: Monday to Friday (Weekends Off)
Votice Period: Immediate to 15 days
We offer a collaborative, innovation-driven work
culture where your contributions directly shape
impactful Al solutions
Send your resume to chitra.borkar@acxtech.co.in
โค2
๐Ÿš€ ๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ๐—ป ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—ฒ๐—ฟ โ€” ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ

Master the hottest skill in tech: building intelligent AI systems that think and act independently.
Join Ready Tensorโ€™s free, hands-on program to create three portfolio-grade projects: RAG systems โ†’ Multi-agent workflows โ†’ Production deployment.

๐—˜๐—ฎ๐—ฟ๐—ป ๐—ฝ๐—ฟ๐—ผ๐—ณ๐—ฒ๐˜€๐˜€๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—ฐ๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป and ๐—ด๐—ฒ๐˜ ๐—ป๐—ผ๐˜๐—ถ๐—ฐ๐—ฒ๐—ฑ ๐—ฏ๐˜† ๐˜๐—ผ๐—ฝ ๐—”๐—œ ๐—ฒ๐—บ๐—ฝ๐—น๐—ผ๐˜†๐—ฒ๐—ฟ๐˜€.

๐—™๐—ฟ๐—ฒ๐—ฒ. ๐—ฆ๐—ฒ๐—น๐—ณ-๐—ฝ๐—ฎ๐—ฐ๐—ฒ๐—ฑ. ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ-๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ถ๐—ป๐—ด.

๐Ÿ‘‰ Join today: https://go.readytensor.ai/cert-610-agentic-ai-certification
โค2
Master the hottest skill in tech: building intelligent AI systems that think and act independently.
Join Ready Tensorโ€™s free, hands-on program to build smart chatbots, AI assistants and multi-agent systems.

๐—˜๐—ฎ๐—ฟ๐—ป ๐—ฝ๐—ฟ๐—ผ๐—ณ๐—ฒ๐˜€๐˜€๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—ฐ๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป and ๐—ด๐—ฒ๐˜ ๐—ป๐—ผ๐˜๐—ถ๐—ฐ๐—ฒ๐—ฑ ๐—ฏ๐˜† ๐˜๐—ผ๐—ฝ ๐—”๐—œ ๐—ฒ๐—บ๐—ฝ๐—น๐—ผ๐˜†๐—ฒ๐—ฟ๐˜€.

๐—™๐—ฟ๐—ฒ๐—ฒ. ๐—ฆ๐—ฒ๐—น๐—ณ-๐—ฝ๐—ฎ๐—ฐ๐—ฒ๐—ฑ. ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ-๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ถ๐—ป๐—ด.

๐Ÿ‘‰ Join today: https://go.readytensor.ai/cert-610-agentic-ai-certification

React โค๏ธ for more free resources
โค2
๐Ÿš€ Hiring: AI / ML Engineer
๐Ÿ“ Princeton, NJ (Hybrid โ€“ 2 days onsite)
Apply: rahul.g@laibatechnology.com
We are looking for a hands-on AI/ML Engineer to design, build, and deploy cutting-edge ML & LLM solutions in production.
Key Responsibilities:
๐Ÿ”น Build & optimize ML/Deep Learning models for large-scale data
๐Ÿ”น Deploy & monitor ML/LLM models using APIs & cloud platforms
๐Ÿ”น Manage AKS clusters, containerized workloads & CI/CD pipelines
๐Ÿ”น Implement model governance, versioning & reproducibility (MLflow, Azure DevOps)
๐Ÿ”น Collaborate with data scientists & domain experts for real-world impact
Tech Stack:
โœ… Python | Docker | Kubernetes | Terraform | Azure ML | Azure OpenAI | AKS | MLflow | Prometheus | Grafana
Must-Haves:
๐Ÿ“Œ Strong experience in ML/LLMs (fine-tuning, prompt engineering, deployment)
๐Ÿ“Œ Cloud expertise in Azure ecosystem (ML, AKS, OpenAI, DevOps)
๐Ÿ“Œ Proven skills in scalable ML infrastructure & CI/CD automation
โค2๐Ÿ”ฅ1
Hiring: Data Scientist (ML & Gen AI)

๐Ÿ“ Location: Hyderabad / Bangalore (Hybrid, 5 days)
๐Ÿ’ผ Experience: 3+ years
๐Ÿ’ฐ CTC: Up to โ‚น21 LPA

Weโ€™re looking for a skilled Data Scientist to work on ML models & Gen AI projects.

โœ… Strong ML/Gen AI experience
โœ… Python, SQL, TensorFlow/PyTorch
โœ… Cloud (AWS/GCP/Azure) knowledge

๐Ÿ“ฉ Apply now: [https://lnkd.in/grzQ3US6]
๐Ÿ‘1
Cyient is hiring!
Position: Data Scientist - Junior
Qualifications: Bachelorโ€™s/ Masterโ€™s Degree
Salary: 6 - 11 LPA (Expected)
Experience: Freshers/ Experienced
Location: Hyderabad, India

๐Ÿ“ŒApply Now: https://careers.cyient.com/cyient/jobview/data-scientist-junior-hyderabad-india-2025081312010289?id=678417
โค3
IBM Summer Internship Program!
Position: Research Intern - AI
Qualifications: Bachelorโ€™s Degree
Salary: 30K - 50K Per Month (Expected)
Batch: 2024/ 2025/ 2026/ 2027
Experience: Freshers
Location: Bangalore; Gurgaon, India (Hybrid)

๐Ÿ“ŒApply Now: https://ibmglobal.avature.net/en_US/careers/JobDetail?jobId=59041&source=WEB_Search_INDIA

๐Ÿ‘‰WhatsApp Channel: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m

๐Ÿ‘‰Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5

All the best ๐Ÿ‘๐Ÿ‘
โค4
Uber is hiring!
Position: Data Scientist, Data Science
Qualification: Bachelor's/ Masterโ€™s Degree
Salary: 16 - 46 LPA (Expected)
Experienc๏ปฟe: Freshers/ Experienced
Location: Bangalore, India

๐Ÿ“ŒApply Now: https://www.uber.com/global/en/careers/list/148160/?uclick_id=191f4c10-ec74-4d28-861c-ecce1809c397

๐Ÿ‘‰WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

๐Ÿ‘‰Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5

All the best ๐Ÿ‘๐Ÿ‘
โค3