CEO @ HiCounselor | 2X O-1 Visa Holder | Ex-Deloitte, PwC | Building a Community to Help Job Seekers with Referrals

I came to the US in 2014 for a master's degree at Duke University.

Post-graduation, I was fortunate to start my career as a Tech Consultant at PwC and then with Deloitte in San Francisco. 

However, I always wanted to utilize my tech skills to start my own business. 

In 2020, I decided to leave my job and launched HiCounselor in San Francisco.

The decision wasn't easy as I had many open questions, concerns about immigration, and fear of failure. 

Fortunately, the decision turned out to be the right one. 
✅ Generated millions in revenue
✅ Raised VC funds from top investors in Silicon Valley
✅ Employ more than 40 people in the US, Canada, and India.

👉 These four years have taught me a lot about starting a company as an immigrant in the US, and I want to share this journey with every technical professional who wants to make side income or become a full-time entrepreneur.

✉️ Join my newsletter (https://lnkd.in/gPBziwZ3), where I share weekly actionable tips. 


👉  If you are not ready to leave your full-time job at this time, that's okay.
You can join hicounselor.com/mentor and make a side income by sharing your technical expertise. 

If you need any help, feel free to message me or comment on my posts. I will respond as soon as possible.

Aditya Sharma

𝗪𝗵𝘆 𝗱𝗼 𝗺𝗼𝗱𝗲𝗹𝘀 𝘀𝘁𝗶𝗹𝗹 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲? 🤔
Most hallucinations are actually RAG stack failures caused by broken context.

When retrieval is weak, the model does exactly what it was trained to do. It completes the pattern using the wrong information.

This is why understanding the full RAG stack matters.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗥𝗔𝗚 𝗦𝘁𝗮𝗰𝗸 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲 👇

1. Ingest & Data Processing
↳ Collect, clean, and structure raw data
↳ Poor ingestion leads to poor retrieval
Tools: Kubeflow, Apache Airflow, Apache NiFi, LangChain Loaders, Haystack Pipelines, OpenSearch

2. Embedding Models
↳ Convert text into semantic meaning
↳ Determines how well the system understands similarity
Tools: HuggingFace Transformers, Sentence Transformers, JinaAI, Cognita, Nomic, LLMWare

3. Vector Databases
↳ Store embeddings efficiently for fast lookup
↳ Enables scalable semantic search
Tools: Milvus, Weaviate, PgVector, Chroma, Qdrant

4. Retrieval & Ranking
↳ Finds the most relevant context for each query
↳ Weak retrieval = confident hallucinations
Tools: FAISS, Weaviate, JinaAI Rerankers, Haystack Retrievers

5. LLM Frameworks & Orchestration
↳ Connect pipelines, tools, and workflows
↳ Ensure reliability end to end
Tools: LangChain, LlamaIndex, Haystack, CrewAI, HuggingFace

6. LLMs (The Responder)
↳ Generate answers grounded in retrieved context
↳ Model size matters less than context quality
Models: LLaMA, Mistral, Phi-2, DeepSeek, Qwen, Gemma

7. Frontend Layer
↳ Where users interact with intelligence
↳ Trust is built at the interface
Tools: NextJS, Streamlit, VueJS, SvelteKit

🔥 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆:
Most hallucinations are not caused by bad models.
They’re caused by bad context.

📌 𝗚𝗲𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗟𝗶𝘀𝘁 (𝗳𝗿𝗲𝗲): https://lnkd.in/gHRjrXQG

👉 Follow me Aditya Sharma for more and 🔄 Repost this to help others use AI

#AI #RAG #OpenSource

Helping Top 1% AI Talent Land Role at Elite AI Companies | Turning Ideas into Real Businesses Using AI | 160k+ Followers | Ex-Deloitte & PwC

Everyone is talking about Agentic AI…
but almost no one tells you when to use which tool.

So I made this simple breakdown 👇
Top 10 Agentic AI tools and where they actually shine.

Most people jump straight into building agents
→ then struggle with chaos, loops, or over-engineering.


𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲:
↳ Some tools are great for automation
↳ Some for multi-agent collaboration
↳ Some for RAG + memory
↳ Some for enterprise workflows
↳ Some just for quick demos


𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 👇

> 𝗻𝟴𝗻 → Use this when you want fast workflow automation, lots of app integrations, or self-hosted control with minimal code.

> 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 → Use this when multiple AI agents need to talk, debate, and collaborate on complex problems.

> 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 → Use this when you’re building real AI products with LLMs, tools, memory, or advanced RAG pipelines.

> 𝗠𝗮𝗸𝗲.𝗰𝗼𝗺 → Use this when non-technical teams need visual, no-code automation across SaaS tools.

> 𝗖𝗿𝗲𝘄𝗔𝗜 → Use this when agents need clear roles (researcher, planner, executor) and structured teamwork.

> 𝗙𝗹𝗼𝘄𝗶𝘀𝗲 → Use this when you want drag-and-drop AI agents or quick RAG prototypes.

> 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 → Use this when agents must plan, remember, loop, and handle complex state-based logic.

> 𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘁𝗮𝗰𝗸 → Use this when you need quick demos with built-in tools, memory, and function calling.

> 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 → Use this when your AI must deeply understand and reason over private or structured data.

> 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 → Use this when you’re building enterprise-grade, scalable, memory-aware AI systems.

The biggest mistake people make?
Using enterprise tools for prototypes
or prototype tools for production

📌 𝗚𝗲𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗟𝗶𝘀𝘁 (𝗳𝗿𝗲𝗲): https://lnkd.in/gHRjrXQG

👉 Follow me Aditya Sharma for more and 🔄 Repost this to help others.

#AgenticAI #AIAgents #AIEngineering

AI Agents are quickly becoming the foundation of modern automation, products, and AI-powered businesses.

In this session of the AI for Everyone series, this beginner-friendly live workshop will help you understand how AI agents actually work, how they learn, and how you can start using them confidently even if you are new to AI.

This is a conceptual + practical fundamentals session, designed to remove confusion and build strong foundations.

What you’ll learn in this 30-minutes live session:

⚡ AI Agent Fundamentals
Understand what AI agents are, how they differ from AI tools, and where they are used.

🤖 How AI Agents Learn and Make Decisions
Learn the core concepts behind memory, tools, reasoning, and automation.

🛠 Real Examples and Workflows
See how AI agents operate in real scenarios across business and personal workflows.

🌟 Getting Started the Right Way
Learn how beginners, professionals, and founders can start building and using AI agents correctly.

Whether you are a student, beginner, recruiter, founder, tech professional, or simply curious about AI, this session will give you clarity and confidence to move forward.

✨ Free to attend. Register now to save your spot.

Session 16: Fundamentals of Learning AI Agents

Data Scientist | 150K LinkedIn | BCBS Of South Carolina | SQL | Python | AWS | ML | Featured on Times Square, Favikon, Fox, NBC | MS in Data Science at UConn | Proven record in driving insights and predictive analytics |

Lead Data Analyst/Data Scientist at Blue Cross Blue Shield of South Carolina, driving revenue growth and operational efficiency through predictive analytics, experimentation, and machine learning.

I build end-to-end data solutions that influence executive decision-making, from churn prediction models and A/B testing frameworks to executive dashboards tracking multimillion-dollar portfolios. My work spans the full ML lifecycle: data pipelines, feature engineering, model deployment, and stakeholder communication.

What Sets Me Apart:

I don't just analyze dat, I translate it into business strategy. Whether it's identifying high-risk customer segments, optimizing conversion funnels through rigorous experimentation, or presenting insights to C-suite leadership, I bridge the gap between technical depth and business impact.

Beyond the 9-to-5:

I'm on a mission to democratize data science education. With 220K+ professionals in my LinkedIn community and 20K+ newsletter subscribers, I share battle-tested insights on SQL, Python, machine learning, and breaking into data science. My content focuses on real-world applications that actually matter in industry, not academic theory.

I've also been recognized nationally:
- Featured in Times Square, Fox, NBC, and major media outlets for career journey
- 3rd Place, Travelers NESS Statathon
- 1st Place, Well4Tech Health Hackathon  
- Favikon recognition for data community contributions

My Technical Stack:

Python (Pandas, Scikit-Learn, XGBoost), SQL, PySpark, R | AWS (S3, Glue, Athena, SageMaker), Azure, Snowflake, Databricks | Tableau, Power BI, Looker | A/B Testing, Bayesian Statistics, Predictive Modeling

Open to discussing data strategy, collaboration, or your journey into data science.

Let's connect: saikumarbysani13@gmail.com

Venkata Naga Sai Kumar Bysani

If you want to stand out as a Data Analyst in 2026, you must build these 5 projects!

(Your portfolio is your resume. Make it count.)

Most aspiring analysts have the same Titanic and Iris datasets on their GitHub. Hiring managers scroll past them in seconds.

These 5 projects show you can handle real-world data pipelines, not just tutorials:

𝟏. 𝐒𝐮𝐩𝐞𝐫𝐬𝐭𝐨𝐫𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
↳ AWS S3, Glue, Athena, SQL, QuickSight
↳ Shows you can work with cloud infrastructure
↳ https://lnkd.in/di8mbpg3

𝟐. 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐓𝐋 𝐏𝐫𝐨𝐣𝐞𝐜𝐭
↳ SQL and Python
↳ Proves you understand the full data lifecycle
↳ https://lnkd.in/d6q5zTwj

𝟑. 𝐍𝐞𝐭𝐟𝐥𝐢𝐱 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐄𝐋𝐓 𝐏𝐫𝐨𝐣𝐞𝐜𝐭
↳ Python and SQL
↳ Data cleaning is 80% of the job. Show you can do it.
↳ https://lnkd.in/dcvhry2q

𝟒. 𝐒𝐐𝐋 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐏𝐫𝐨𝐣𝐞𝐜𝐭
↳ Pure SQL
↳ SQL is non-negotiable. This proves mastery.
↳ https://lnkd.in/dZwSiQRy

𝟓. 𝐘𝐞𝐥𝐩 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐑𝐞𝐯𝐢𝐞𝐰𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
↳ S3, Python, Snowflake, SQL
↳ Combines cloud + modern data warehouse skills
↳ https://lnkd.in/dguyNe_Y

𝐖𝐡𝐲 𝐭𝐡𝐞𝐬𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐰𝐨𝐫𝐤:
→ They use tools companies actually hire for
→ They show end-to-end thinking, not just analysis
→ They prove you can work with messy, real data

Start with these to build your foundation. Then move on to creating your own projects based on problems you actually care about.

That's when your portfolio goes from "good" to "memorable."

Which project are you starting with? 👇

♻️ Repost to help someone land their first data role

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 21,000+ here → https://lnkd.in/dUfe4Ac6

Data Scientist | 3+ years in Predictive Analytics, Experimentation & Business Impact | 200K+ Data Community | Featured on Times Square, Fox, NBC

I just built a free interview prep guide for data analysts and data scientists.

And I'm giving it away.

Most people preparing for data interviews are:
↳ Scrambling the night before with no real plan
↳ Googling random questions with no structure
↳ Watching 10-hour YouTube playlists, they never finish

I used Notion Agent to build something better.

𝐖𝐡𝐚𝐭'𝐬 𝐢𝐧𝐬𝐢𝐝𝐞:

↳ SQL questions (from basics to window functions)
↳ Python and Pandas essentials
↳ Statistics and probability concepts
↳ Behavioral interview frameworks
↳ Real company questions with solutions

I used Notion Agent to turn my interview knowledge into a structured guide.

→ Created the entire database structure in minutes
→ Organized questions by difficulty, topic, and company
→ Built custom views for different study approaches
→ Added resources and explanations across 100+ entries

What would have taken me days took one afternoon.

2026 is about building resources that actually help people land data roles.

Comment "𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰" and I'll send you the guide.

Let Notion Agent do your work for you at https://ntn.so/SaiBysani

♻️ Repost to help someone preparing for data interviews right now.

This post is sponsored by Notion.

#NotionPartner #NotionNewYear

January is the best time to job hunt. Most people will still blow it.

Companies are unfreezing budgets. New headcount is opening up. Recruiters are back from PTO.

But here's the problem: your resume still isn't ready.

You'll spend hours perfecting it. Submit to 50+ jobs. Hear nothing back.

Not because you're unqualified, but because an algorithm filtered you out before a human ever saw it.

So I built "𝐑𝐞𝐬𝐮𝐦𝐞 𝐌𝐚𝐭𝐜𝐡" to fix this: 
An AI tool that compares your resume against job descriptions and shows you exactly what's missing.

→ Missing skills ranked by priority (High/Medium/Low)
→ Specific guidance on 𝐖𝐇𝐄𝐑𝐄 and 𝐇𝐎𝐖 to add them
→ Learning resources for gaps you need to fill

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐜𝐫𝐚𝐳𝐲 𝐩𝐚𝐫𝐭:

I built this entire app on Replit by typing what I wanted in plain English. No coding required.

𝐑𝐞𝐩𝐥𝐢𝐭'𝐬 AI Integrations gave me instant access to OpenAI, Anthropic, Gemini, all the top models. No API keys. No setup headaches. Just picked one and started building.

𝐖𝐡𝐚𝐭 𝐑𝐞𝐩𝐥𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞𝐝:
→ Turned my description into working code
→ Set up AI integration automatically
→ Deployed it instantly, ready to share

This is vibe coding. You describe it. Replit builds it. You ship it.

New year, new satisfying way to solve your own problems.

Comment "𝐑𝐞𝐬𝐮𝐦𝐞" and I'll send you the link to try it yourself.

𝐘𝐨𝐮 𝐜𝐚𝐧 𝐮𝐬𝐞 𝐦𝐲 𝐥𝐢𝐧𝐤 𝐭𝐨 𝐠𝐞𝐭 $10 𝐜𝐫𝐞𝐝𝐢𝐭𝐬: https://lnkd.in/dywbyxB9

(It is eligible for people who purchased Replit Core.)

♻️ Repost if someone in your network is job hunting in 2026

I am hosting a free live workshop on building real-world data analytics projects with Chris.

Most aspiring analysts know they need a portfolio. But very few know what actually impresses hiring managers.

I've reviewed hundreds of portfolios. Most look the same: Titanic, Iris, MNIST. Tutorial follow-alongs with no business context.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞'𝐥𝐥 𝐜𝐨𝐯𝐞𝐫:

→ Why portfolios matter and what hiring managers actually look for
→ How to choose the right projects for data analyst roles
→ How to structure an end-to-end analytics project
→ How SQL, Tableau, Python, and spreadsheets work together in real workflows
→ How to tell a compelling story with data

𝐋𝐢𝐯𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐁𝐮𝐢𝐥𝐝:

In the second half, Chris Perry and I will build a complete analytics project live:
→ Data ingestion and preparation
→ Analysis using SQL/Python
→ Visualization and dashboarding
→ Explaining every decision along the way

This is not a theory-heavy session. You'll see real tools, real workflows, and real decision-making, exactly how analytics is practiced in the industry.

𝐃𝐞𝐭𝐚𝐢𝐥𝐬:
📅 Saturday, January 17
⏰ 12:00 PM – 2:00 PM EST

𝐖𝐡𝐨 𝐭𝐡𝐢𝐬 𝐢𝐬 𝐟𝐨𝐫:
→ Aspiring and early-career data analysts
→ Career switchers breaking into analytics
→ Analysts who want to level up their portfolio

Your portfolio is your proof of work. Make it count.

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://luma.com/q0sezwmh

Are you joining?

♻️ Repost if someone in your network is building their data analytics portfolio

Perfect analysis. Beautiful dashboard. Zero impact. Sound familiar?

Not because the data is wrong. Because the visualization quietly misleads, overwhelms, or confuses.

Here are 6 data visualization mistakes that make stakeholders ignore your insights, even when the numbers are correct 👇

𝟏. 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐛𝐚𝐫 𝐜𝐡𝐚𝐫𝐭𝐬 𝐚𝐰𝐚𝐲 𝐟𝐫𝐨𝐦 𝐳𝐞𝐫𝐨 Makes small changes look dramatic and distorts perception.

𝟐. 𝐔𝐬𝐢𝐧𝐠 𝐩𝐢𝐞 𝐜𝐡𝐚𝐫𝐭𝐬 𝐟𝐨𝐫 𝐭𝐨𝐨 𝐦𝐚𝐧𝐲 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬 Human eyes can't accurately compare angles and areas.

𝟑. 𝐎𝐯𝐞𝐫𝐮𝐬𝐢𝐧𝐠 𝐜𝐨𝐥𝐨𝐫𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐦𝐞𝐚𝐧𝐢𝐧𝐠 Every extra color adds cognitive noise instead of clarity.

𝟒. 𝐃𝐮𝐚𝐥 𝐚𝐱𝐞𝐬 𝐭𝐡𝐚𝐭 𝐢𝐦𝐩𝐥𝐲 𝐟𝐚𝐥𝐬𝐞 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 Scale manipulation can manufacture relationships that don't exist.

𝟓. 𝐂𝐡𝐚𝐫𝐭 𝐭𝐢𝐭𝐥𝐞𝐬 𝐭𝐡𝐚𝐭 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞, 𝐧𝐨𝐭 𝐜𝐨𝐧𝐜𝐥𝐮𝐝𝐞 If the title doesn't state the insight, readers must guess.

𝟔. 𝐒𝐡𝐨𝐰𝐢𝐧𝐠 𝐚𝐥𝐥 𝐝𝐚𝐭𝐚 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭 Executives want conclusions, not raw tables.

The common thread? Data doesn't speak for itself. Design decisions decide what people believe.

Good visualization isn't about being fancy. It's about being honest, clear, and decisive.

If your charts don't guide attention, they invite misinterpretation.

Which one are you guilty of? 
♻️ Repost if someone in your network builds dashboards

—
📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

Data Visualization Mistakes to Avoid

Stop manually extracting data from websites that don't have APIs.

There's a better way now.

As data professionals, we've all been there. You need data from a website, there's no API, and you're stuck writing brittle extraction scripts that break every time the UI changes.

I've been testing Amazon Nova Act, and it's built for exactly this problem.

𝐖𝐡𝐚𝐭 𝐢𝐭 𝐝𝐨𝐞𝐬:

Instead of writing CSS selectors and XPath queries, you tell it what to do in plain English:
→ "Go to this page and extract all the pricing data"
→ "Fill out this form with these values"
→ "Navigate through these filters and download the results"

Nova Act handles the browser automation, retries, and state. You focus on the data.

𝐖𝐡𝐲 𝐝𝐚𝐭𝐚 teams 𝐬𝐡𝐨𝐮𝐥𝐝 𝐜𝐚𝐫𝐞:

→ No more maintaining fragile Selenium scripts
→ Extract data from dashboards and portals without APIs
→ Automate repetitive data entry across multiple systems
→ Run smoke tests to validate dashboards and reports after deployments
→ Works with Python, so it fits right into your workflow

This isn't about flashy AI demos. It's about automating the boring, repetitive stuff that eats up your time. Amazon calls them "NormcoreAgents", unsexy but reliable automation that actually works at scale.

𝐇𝐨𝐰 𝐭𝐨 𝐭𝐫𝐲 𝐢𝐭:

1. Start in the web playground (no setup needed)
2. Build your agent using natural language
3. Download the Python script
4. Customize in your IDE

𝐓𝐫𝐲 𝐨𝐮𝐭 𝐍𝐨𝐯𝐚 𝐀𝐜𝐭 → https://fandf.co/4q3jVOc

Thanks to Amazon for sponsoring this post.

What’s the most annoying website or dashboard you’ve ever had to extract data from? 👇

♻️ Repost if someone in your network spends too much time on manual data collection

#AmazonPartner

Stop opening 47 browser tabs to prep for ML interviews.

You bookmark 12 YouTube playlists. You save 8 Medium articles "for later."

Then you close your laptop and do nothing.

I've been there. Most of us have.

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦:

ML interview prep is scattered everywhere. A bit of system design here. Some LeetCode tips there. Random probability questions somewhere else.

No structure. No roadmap. Just chaos.

𝐓𝐡𝐚𝐭'𝐬 𝐰𝐡𝐲 𝐈 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐬𝐡𝐚𝐫𝐞 𝐭𝐡𝐢𝐬 𝐆𝐢𝐭𝐇𝐮𝐛 𝐫𝐞𝐩𝐨:

It's called "Machine Learning Interview" by Khang Pham. He's a SWE/ML with 10 years of experience and got offers from 𝐆𝐨𝐨𝐠𝐥𝐞, 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧, 𝐒𝐧𝐚𝐩, 𝐚𝐧𝐝 𝐒𝐭𝐢𝐭𝐜𝐡𝐅𝐢𝐱.

The repo has 11.7k+ stars for a reason.

𝐖𝐡𝐚𝐭'𝐬 𝐢𝐧𝐬𝐢𝐝𝐞:

→ ML System Design (YouTube recommendations, LinkedIn feed ranking, ad click prediction)
→ LeetCode questions organized by category for MLEs
→ Statistics and probability cheat sheets
→ ML fundamentals (logistic regression, random forests, boosting, PCA)
→ Deep learning essentials
→ Real interview questions from FAANG companies
→ AB testing resources
→ Company-specific guides

Everything in one place. No more 47 tabs.

𝐖𝐡𝐨 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐟𝐨𝐫:

→ Data Scientists preparing for MLE roles
→ ML Engineers switching companies
→ Anyone targeting FAANG or top tech companies

The author literally documented what it takes to get offers from top companies. That's rare.

Link in comments 👇

What's your biggest challenge when prepping for ML interviews?

♻️ Repost if someone in your network is preparing for ML interviews

A model can show 99% accuracy and still be completely useless.

That's the trap imbalanced data creates.

In production, that means missed fraud. Missed failures. Missed diagnoses. Bad business decisions.

When one class dominates, your model learns to be lazy, predicting the majority every time and still looking "accurate" on paper.

This post breaks down what actually works when your data isn't balanced.
𝐒𝐭𝐚𝐫𝐭 𝐡𝐞𝐫𝐞 (𝐪𝐮𝐢𝐜𝐤 𝐟𝐢𝐱𝐞𝐬):

→ 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐜𝐨𝐬𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐧𝐨𝐭 𝐝𝐚𝐭𝐚 𝐛𝐚𝐥𝐚𝐧𝐜𝐞 False positives and false negatives don't hurt equally. Optimize for the mistake you can't afford.

→ 𝐂𝐥𝐚𝐬𝐬 𝐰𝐞𝐢𝐠𝐡𝐭𝐬 𝐛𝐞𝐟𝐨𝐫𝐞 𝐚𝐧𝐲𝐭𝐡𝐢𝐧𝐠 𝐟𝐚𝐧𝐜𝐲 A 30-second change that works surprisingly often.

→ 𝐅𝐢𝐱 𝐲𝐨𝐮𝐫 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 Accuracy hides failure. Precision, recall, F1, and confusion matrices don't.

𝐖𝐡𝐞𝐧 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐦𝐨𝐫𝐞 𝐟𝐢𝐫𝐞𝐩𝐨𝐰𝐞𝐫:

→ 𝐒𝐌𝐎𝐓𝐄, 𝐮𝐬𝐞𝐝 𝐜𝐚𝐫𝐞𝐟𝐮𝐥𝐥𝐲 Synthetic samples help, but only on training data. Never test data.

→ 𝐔𝐧𝐝𝐞𝐫𝐬𝐚𝐦𝐩𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐦𝐚𝐣𝐨𝐫𝐢𝐭𝐲 Sometimes throwing data away makes models faster and better.

→ 𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐢𝐧𝐠 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬 More compute, but consistently stronger results than single resampling.

→ 𝐑𝐞𝐟𝐫𝐚𝐦𝐞 𝐚𝐬 𝐚𝐧𝐨𝐦𝐚𝐥𝐲 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 Stop classifying when "rare" truly means "abnormal."

𝐌𝐲 2 𝐜𝐞𝐧𝐭𝐬: There is no universal solution. Some problems are solved with class weights. Others need ensembles. Sometimes the only honest answer is more minority data, boring, slow, but effective.

If you work on fraud, churn, risk, defects, or rare-event prediction, this is one of those topics that quietly decides whether your model helps or harms.

What else would you add?

♻️ Repost if someone in your network works with imbalanced datasets

📘 Get 150+ real interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

📬 I share tips on data analytics & career growth in my free newsletter. Join 20,000+ readers → https://lnkd.in/dUfe4Ac6

Handling Imbalance Data

5 things I wish I knew before starting my Data Science career (would have saved me a year!)

I learned these the hard way. You don't have to.

𝟏. 𝐒𝐐𝐋 > 𝐅𝐚𝐧𝐜𝐲 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬
↳ I spent months learning complex ML models
↳ Meanwhile, 80% of my job was writing SQL queries
↳ Master the fundamentals first. They never go out of style.

𝟐. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 > 𝐂𝐨𝐝𝐞
↳ Your model is useless if stakeholders don't understand it
↳ Learn to explain complex things simply
↳ The best data scientists are translators, not just coders

𝟑. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 > 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐞𝐬
↳ I collected certificates like trophies
↳ Hiring managers barely glanced at them
↳ A solid portfolio speaks louder than any credential

𝟒. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐦𝐩𝐚𝐜𝐭 > 𝐌𝐨𝐝𝐞𝐥 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲
↳ Nobody cares if your model is 95% accurate
↳ They care if it saved money or drove revenue
↳ Always tie your work back to business outcomes

𝟓. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠 > 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠 𝐁𝐥𝐢𝐧𝐝𝐥𝐲
↳ My best opportunities came through connections
↳ Not from submitting 100+ applications
↳ Build relationships before you need them

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
The technical stuff is the easy part. The career stuff? That's where most people struggle.

Focus on skills that compound over time: communication, business thinking, and relationships.

Which of these hit home for you? 👇

♻️ Repost to help someone starting their data career

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 21,000+ here → https://lnkd.in/dUfe4Ac6

I've watched 3-week analyses get ignored in 3-minute meetings


You can build the cleanest dashboard. Run the most advanced analysis. And still… nothing changes.

Not because the numbers are wrong. Because the right business questions were never asked.

𝐁𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐬𝐭𝐚𝐫𝐭:

→ What decision will this influence?
If no decision changes, the analysis adds no real value.

→ Who is the decision maker?
Insights need an owner; otherwise, they die in slides.

→ Why does this problem matter now?
Timing defines relevance more than sophistication.

→ What would success look like?
Without clear success criteria, results are meaningless.

𝐃𝐮𝐫𝐢𝐧𝐠 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬:

→ What action will be taken if numbers change?
Metrics without actions create dashboards, not outcomes.

→ What assumptions are we making?
Unchallenged assumptions lead to confident mistakes.

→ What is the cost of being wrong?
False positives and false negatives are not equal.

→ What data do we actually need?
More data often adds noise, not clarity.

𝐁𝐞𝐟𝐨𝐫𝐞 𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐢𝐧𝐠:

→ What context is missing from the numbers?
Seasonality, market shifts, and policy changes change everything.

→ How will this insight be communicated?
If it can't be explained simply, it won't be used.

Good analysts don't start with data. They start with decisions, actions, and consequences.

For me, the most skipped question is "What decision will this influence?" I've seen entire projects die because no one asked it upfront.

Which one do you see skipped most often?

♻️ Repost if someone in your network works with data

—
📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

I just analyzed 1,000+ job postings for data roles in 2026. Here's what I found.

(The market has completely shifted.)

Companies aren't hiring the way they used to. Welcome to the "precision hiring" era. Fewer roles, higher expectations, bigger paychecks for specialists.

I used Superagent, a newly launched AI product from Airtable, to research this. Right away, multiple specialized AI agents worked simultaneously to pull from sources like LinkedIn Jobs, Glassdoor, and industry reports, to deliver an interactive 𝐒𝐮𝐩𝐞𝐫 𝐑𝐞𝐩𝐨𝐫𝐭 with salary benchmarks, skill trends, and hiring patterns.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐬𝐡𝐨𝐰𝐬:

𝟐𝟎𝟐𝟔 𝐒𝐚𝐥𝐚𝐫𝐲 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 (𝐌𝐢𝐝𝐩𝐨𝐢𝐧𝐭):
↳ Data Analyst: $117,250 (+3.3% YoY)
↳ Data Scientist: $153,750 (+4.1% YoY)
↳ Data Engineer: ~$145,000
↳ ML Engineer: $170,750 (+4.4% YoY)

𝐓𝐡𝐞 𝐒𝐤𝐢𝐥𝐥𝐬 𝐒𝐡𝐢𝐟𝐭:
↳ 60% of Data Scientist postings now require AI skills
↳ LLM experience is the #1 demanded AI skill
↳ Python + SQL are still non-negotiable
↳ Cloud platforms (AWS, Snowflake) showing up in 41% of DS roles

𝐁𝐢𝐠𝐠𝐞𝐬𝐭 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬:
→ ML Engineer is the #1 fastest-growing role in the US (per LinkedIn)
→ Entry-level roles are shrinking. Companies want production-ready skills.
→ AI-skilled professionals command 5-12% salary premiums
→ The market isn't saturated, it's "misaligned". Employers want judgment, not just theory.

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
The days of learning Python and landing a data job are fading. 2026 rewards specialists who can ship end-to-end, work with AI tools, and make decisions under uncertainty.

If you're job hunting this year, focus on production experience over certifications.

If you want to check out the full Super Report I generated, comment “𝐑𝐞𝐩𝐨𝐫𝐭” below and I'll send it to you. The full report includes interactive salary charts, geographic breakdowns, and top hiring companies. All with citations you can actually verify

𝐓𝐫𝐲 𝐢𝐭: https://lnkd.in/dBMd5wTU

Use my code "VENKATA2FREE" to get 2 months free for Superagent!

Which role are you targeting in 2026? 👇

♻️ Repost to help someone plan their data career this year

#SuperagentPartner
Airtable Superagent

Brand partnership • Data Scientist | 3+ years in Predictive Analytics, Experimentation & Business Impact | 200K+ Data Community | Featured on Times Square, Fox, NBC

I'm hosting a FREE workshop on how to ACE your next data interview.

(Stop tweaking your resume. Start building proof.)

Most candidates fail data interviews not because they lack skills, but because they don't know what to focus on or how to show it.

In this session, I'm breaking down the exact roadmap hiring managers expect in 2026:

𝐖𝐡𝐚𝐭 𝐰𝐞'𝐥𝐥 𝐜𝐨𝐯𝐞𝐫:
→ The full interview process (what actually happens)
→ SQL exercises with real practice problems
→ Business sense questions and how to answer them
→ Behavioral interview frameworks that work
→ The What, Why, How framework for any question

This is for you if:
↳ You're preparing for data analyst or data scientist interviews
↳ You keep getting rejected without knowing why
↳ You want a clear, practical prep plan, not random LeetCode grinding

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://luma.com/yic2lm38

See you there 👋

♻️ Repost to help someone preparing for their next data interview

—

📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6
Activate

More than 400 people registered for our Bay Area Data & AI meetup.

1 day left to join us.

This is a casual gathering for people working in data, AI, and product. We'll talk about trends in data, how roles are shifting, the evolution of skills, network, and swap stories about what we're excited about.

𝐃𝐞𝐭𝐚𝐢𝐥𝐬:
📅 Saturday, January 24
⏰ 10:00 AM PST

𝐇𝐨𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐦𝐞:
→ Manisha Arora
→ Rami Krispin
→ Vishakha Sadhwani
→ Sriram Arumelli
→ Devshree Bharatia

If you're navigating the AI shift in data roles, this is the room to be in.

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://luma.com/kgsx6o61

Thanks to Findzo for sponsoring this event! They help teams spot where AI conversations are failing, like identifying why a support bot's escalations were spiking and what to fix. Check them out → https://www.findzo.ai

See you all tomorrow 👋

♻️ Repost if someone in your network works in data or AI in the Bay Area

AI can write code in seconds. Shipping it still takes hours.


Claude Code, Windsurf, Curso, the demos look incredible. But here's what nobody talks about: the last mile.

You can generate beautiful code. Build something that works locally. And then… it just sits there. On your machine. Unseen. Unshipped.

Not because the code is bad. Because shipping is still a pain.

𝐓𝐡𝐞 𝐠𝐚𝐩 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 "𝐢𝐭 𝐰𝐨𝐫𝐤𝐬" 𝐚𝐧𝐝 "𝐢𝐭'𝐬 𝐥𝐢𝐯𝐞":

→ Setting up hosting, Configuring databases
→ Managing secrets and environment variables
→ Deploying without breaking everything
→ Making it shareable with a real URL

This is where most AI-built projects die.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐈'𝐯𝐞 𝐛𝐞𝐞𝐧 𝐝𝐨𝐢𝐧𝐠:

I use 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞 for the deep work, writing logic, refactoring, working through complex code. It's powerful for that.

But when it's time to ship? I move to 𝐑𝐞𝐩𝐥𝐢𝐭.

→ Runs in the browser. No local setup headaches.
→ Databases, secrets, and storage are built-in.
→ One-click deploy. Real URL. Actually live.
→ Works with Claude Code through GitHub sync.

Claude Code handles the thinking. Replit handles the shipping.

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬: The best AI coding setup isn't about picking one tool. It's about combining the right ones. Claude Code for control. Replit for getting it out the door.

If your projects keep dying on localhost, this combo might be worth trying.

What's your shipping workflow? Drop it below.

𝐘𝐨𝐮 𝐜𝐚𝐧 𝐮𝐬𝐞 𝐦𝐲 𝐥𝐢𝐧𝐤 𝐭𝐨 𝐠𝐞𝐭 $10 𝐜𝐫𝐞𝐝𝐢𝐭𝐬: https://lnkd.in/dywbyxB9

(It is eligible for people who purchased Replit Core.)

♻️ Repost if someone in your network builds with AI

Data scientists won't be replaced by AI. They'll be replaced by data scientists who know how to use AI.

I've seen it firsthand. Data scientists who were "𝐬𝐞𝐧𝐢𝐨𝐫" two years ago are now competing with juniors who know how to automate workflows with AI.

If you're still doing manual EDA, writing the same SQL queries, and building dashboards one at a time, you're already behind.

That's why I'm recommending the 𝐅𝐮𝐭𝐮𝐫𝐞𝐏𝐫𝐨𝐨𝐟 𝐀𝐈 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐁𝐨𝐨𝐭𝐜𝐚𝐦𝐩 (Jan 2026 Cohort) by Andres Vourakis with 7 years of experience in AI/ML.

This isn't another "intro to ChatGPT" course. It's a 6-week deep dive into designing and automating workflows that 10x your impact.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐥𝐞𝐚𝐫𝐧:

→ Semantic Layers & Data-Aware AI (build text-to-SQL that actually works)
→ AI Coding Environments & MCP (set up Cursor for production-ready development)
→ Agents vs Workflows (know when to use each, build with LangGraph)
→ EDA Agent & A2A Protocol (automate exploratory analysis)
→ System Design for Agentic Systems (break down AI into clear, logical components)
→ Capstone: Build a Talk-to-Your-Data Slackbot from scratch

𝐖𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐛𝐞 𝐚𝐛𝐥𝐞 𝐭𝐨 𝐝𝐨 𝐚𝐟𝐭𝐞𝐫:

→ Design AI workflows that handle repetitive data tasks
→ Build agents that surface insights automatically
→ Ship production-ready tools your stakeholders can actually use
→ Stand out in interviews with real agentic projects

𝐃𝐞𝐭𝐚𝐢𝐥𝐬:
📅 Jan 23 - Mar 5 (6 weeks)
🎟️ Use code 𝐉𝐀𝐍𝟐𝟔-𝟐𝟓-𝐒𝐀𝐈 for 25% off
⏰ Enrollment closes January 19, 2025

𝐄𝐧𝐫𝐨𝐥𝐥 𝐡𝐞𝐫𝐞 → https://futureproofds.com/

𝐌𝐲 2 𝐜𝐞𝐧𝐭𝐬: This bootcamp teaches you to be the one doing the replacing, not the one getting replaced.

Are you leveling up your AI workflow skills this year?

♻️ Repost if someone in your network is in data or AI

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The Battle

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