Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

As an AI engineer, LLM Twin is an important architecture to understand.

An LLM Twin refers to a system where a language model is designed to represent the knowledge, writing patterns, and decision logic of a specific person, team, or organization. The goal is consistency and reliability, not novelty.

At its core, an LLM Twin is built around how information flows. Think of it as 4 steps:

1️⃣ First, content is collected from real sources: blogs, internal documents, posts, code repositories, or knowledge bases. This data reflects how the person or organization actually thinks and communicates.

2️⃣ Next, the data is structured for two purposes.
One path prepares instruction-style examples that help the model learn how to respond.
Another path prepares retrieval data so the system can pull relevant context at inference time. This ensures responses stay grounded in real material.

3️⃣ Then comes training and evaluation. Models are fine-tuned where appropriate, tested against clear criteria, and versioned before deployment. This step looks very similar to traditional ML workflows, just adapted for language models.

4️⃣ Finally, at inference time, user requests are combined with retrieved context and passed through the deployed model. Monitoring is critical here: quality, latency, and cost all need to be observed continuously.

Where is this used in practice?

✦ Personal or creator assistants that help scale writing and communication
✦ Company-wide assistants that maintain a consistent product or brand voice
✦ Internal tools that answer questions based on code, docs, or prior decisions
✦ Support systems that reflect how experienced engineers respond to issues

Why should AI Engineers understand this?
This architecture reflects how GenAI systems are actually built and maintained. It forces you to think beyond prompts and focus on data quality, retrieval design, evaluation, and long-term system behavior.

My 2 cents 🤌
This is a GREAT topic to build a portfolio project around!! 

Credit: LLM Engineer’s Handbook by Paul Iusztin  and Maxime Labonne. I really like how this book breaks down AI engineering clearly and goes deep into different GenAI system architectures without hand-waving.

Over the past few months, Arvind and I've delivered seven deep technical workshops across agentic AI, RAG, context engineering, AI evals, MCP, vibe coding, and more.

One theme kept coming up: you want something more structured. Not just standalone sessions, but a clear end-to-end roadmap from A to Z.

So that’s what we’re building.

A structured, comprehensive course for:
- Hands-on builders: software, AI, and ML engineers
- AI product managers
- Tech leaders and CXOs looking to upskill meaningfully

Before we finalize it, I’d love your input. I’ve created a short survey to understand where you are, what you want to learn, and your career goals.

It takes under 10 minutes.

As a thank you, you’ll receive an additional 10% discount when we launch the course soon 🫶

Survey: https://lnkd.in/gQYeBCRP

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Here’s my honest pattern with almost every AI tool I try.

I end up using a lot of tools, usually early access. After repeating this cycle enough times, I realized my reactions are almost predictable.

👉First hour
There’s real excitement.
I’m clicking around, testing odd prompts, trying things that used to fail. No strategy yet, just my curiosity and momentum.

👉Next few hours
I start comparing.
Where would this actually fit in my work?
What would I stop using if I kept this?
Is this clearly better, or just new?

👉Later that day
I experiment without guidance.
I skip the docs and examples on purpose. I try incomplete ideas, messy workflows, prompts I would never put in a demo. This tells me more than any tutorial ever could.

👉Day two
Then I look at the examples the team shared.
They usually work smoothly. They’re polished. They show the tool at its best, which is exactly what they’re meant to do.

👉After a few days
This is where the real understanding kicks in.
I start tweaking those examples. Changing assumptions. Using it in ways that feel realistic, not ideal. That’s when the boundaries show up. Not frustration. Just clarity.

Here’s the takeaway I’ve learned the hard way:
If you haven’t noticed limitations yet, you probably haven’t pushed the tool very far.

And honestly, if I use something consistently for three days and still can’t find a meaningful flaw, that’s rare. That earns real respect.
I haven’t experienced that yet.

Curious how others see this.
Are there tools you’ve used deeply where the limits still haven’t shown up?

Just a couple of weeks ago, Arvind and I hosted an in-person AI meetup with The Hub Bengaluru.

Over 300 people showed up in person.

Every single one of them was either already working in AI or actively trying to break into the space. The energy in the room was unreal. It genuinely felt like a room full of builders, practitioners, and people who are curious enough to go deep.

During the event, we rolled out a small challenge. We asked attendees to share a recap of what they learned, and we said we would pick one winner.

Really excited to share that Suraj V is that lucky winner 🎉
Suraj gets free access to all the The Gen Academy workshops we have hosted so far.

Huge congratulations, Suraj V!! 

Building The Gen Academy has always come from a very real observation that Arvind and I see every single day in our work. The adoption of generative AI and LLMs is moving incredibly fast, but the depth of AI engineering skills needed to build reliable systems is not keeping up.

There is a very real skill gap in AI engineering right now! There are so many curious souls who want to become AI engineers- but they aren't able to get those opportunities; and there are so many companies out there who  want to hire, but aren't finding the right talent. 

Our mission is to bridge that skill-gap!

As people who are deeply involved in this space, we felt a responsibility to help bridge that gap. Not just for people who are already experienced, but also for entry-level folks who want to build strong foundations the right way.

That is why our workshops are intentionally designed to be both intuitive and practical. We focus on theory that actually helps you reason about systems, and we pair that with hands-on demos where you build real things using modern toolkits and frameworks.

So far, we have hosted 6 workshops, and the response has been incredibly encouraging. We have trained 1500+ people already ❤️ 

Our next workshop is coming up on February 15th, and it will be focused on AI evaluations (link in comments)

Do follow The Gen Academy to stay updated on all our upcoming workshops.

Six months ago, Arvind and I started The Gen Academy with one simple goal- to create workshops that are deeply-technical, practical, cuts through the ton of noise out there, and that provides people clarity on building productive-grade AI systems.

So far we have trained 2100+ people. We’ve hosted 7 workshops, and consistently we’ve had 300–400 builders join us live.

The audience has been incredible.
- Software engineers moving into AI.
- AI engineers building LLM systems in production.
- Product leads thinking about reliability and evaluation.
- Directors and VPs from top tech and Fortune 500 companies who want to truly understand what’s under the hood.
- Even people from non-tech background who wanted to strengthen their technical fundamentals 

Recently, we hosted a workshop on AI Evaluations.

And this one felt especially important.

Because evaluating non-deterministic generative systems is fundamentally different from traditional ML evaluation.

We covered:

✦ How to evaluate non-deterministic generative AI systems
✦ How to think about benchmarks and what they actually tell you
✦ How to design your own evaluation dataset
✦ How to choose the right metrics for qualitative and system-level evaluation
✦ How to combine human evals, code-based evals, and LLM-as-a-judge
✦ A hands-on walkthrough so this isn’t just theory

If you’re building LLM applications, RAG pipelines, or agentic systems, evaluation is not optional. It is the difference between a demo and a reliable product.

If you missed the live session, we’ve shared the recording here: https://lnkd.in/gzMdbfWH

We’ll continue building these workshops for engineers who want to go beyond experimentation and into production.

More coming soon, so do follow us & The Gen Academy to get updates on our next workshops.

5 days to go! 
Arvind and I are super excited to be hosting an AI evals hands-on workshop on February 15.

It’s a 90-minute session where we’ll cover the fundamentals of AI evaluations and then actually do it together. You’ll walk away having built and reasoned through real evals yourself. 

If you’re aspiring to be an AI engineer or planning to upskill for AI roles in 2026, understanding how to evaluate LLM applications is critical.

Building an LLM app is often the easy part. Knowing whether it truly works in production is where most teams struggle.

LLM evaluation is not just about correctness anymore. You are judging usefulness, safety, faithfulness to sources, consistency across runs, cost, latency, and real user outcomes across prompts, RAG pipelines, tools, and agentic workflows.

Because this is hard, many teams fall back to vibe-evals. If a few outputs look good, they ship. That approach breaks quickly when models change, prompts regress, retrieval fails, or user behavior shifts. Without evals, problems surface only after users complain.

Why learning evals matters:
✦ Evals turn LLM development from guesswork into engineering
✦ They help catch regressions as models, prompts, or tools evolve
✦ They are essential for scaling RAG systems and agentic workflows
✦ In 2026, strong teams use hybrid evals combining automated metrics, LLM-as-a-judge, and targeted human review

Evals are no longer optional. They are a core AI skill.

In this workshop, Arvind and I will walk through how AI evals are done in real systems, with hands-on exercises so you build intuition.

This is designed for AI engineers and tech leads building systems, and even business leaders who want intuition for what “good” really means in production. Even if you are a beginner, don't worry, we will give you the foundations for you to understand everything comprehensively, and enough resources that you can review at your own pace.

If you want to level up your AI skills for 2026, register here:
https://lnkd.in/dQSR7yhU

Evaluation Resources

Fine-tuning is going to be one of the biggest focus areas for AI engineering teams this year.

We’re past the stage where calling an API is enough. The teams creating real leverage are the ones adapting models to their own data, workflows, and edge cases. Out-of-the-box models are powerful, but they are generic. Fine-tuning is where enterprises unlock meaningful efficiency gains and build real differentiation.

If you want to upskill in AI engineering, this is a topic you cannot skip.
Arvind and I are hosting a deep, hands-on workshop on Fine-Tuning LLMs.

In 90 minutes, you’ll learn:
✦ When fine-tuning makes sense and when it does not
✦ How SFT, RLHF, and DPO fit into real-world pipelines
✦ An end-to-end, code-based demo from raw data to a specialized model
✦ How to evaluate true performance improvements and avoid production pitfalls

This session is designed for software engineers, AI engineers, and data scientists, and it is equally valuable for product managers, solutions architects, tech leads, and executives working closely with model ecosystems.

If you want to move from using LLMs to actually optimizing them, this workshop will give you a practical framework to do it: https://lnkd.in/g-5gHrsY

In 2026, AI isn’t judged by a wow factor anymore.
It’s judged by whether it actually improves your workflows.

One of the most common questions I get from founders and teams I advise is how to automate go-to-market and sales properly. Real GTM automation needs deep ICP research, clear definitions, personalized messaging, and credible sources. This isn’t a one-prompt problem.

Normally, building a solid GTM or sales report would take me several hours, even with existing AI tools helping with research.

I recently got early access to Airtable’s new Superagent, and what impressed me was the approach. It didn’t treat my request as a single task. It broke it into ~16 steps, ran multiple agents in parallel, did deep research per subtask, cited sources, and stitched everything together into one structured output called a Super Report.

I used it to build a technical, informational report on AI for GTM/Sales in minutes. No fluff. No hallucinations. Just deeply researched content I could actually use with business leaders.

If your work involves building reports, decks, or MVP websites that need accuracy and depth, this feels less like “AI that makes things pretty” and more like AI that does real work.

Do check it out here: https://lnkd.in/gDkfSSG2

PS: You can get 2 months free for @Superagent, using code: AISH2FREE

#SuperagentPartner, #ad, #SuperagentbyAirtable

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

A close friend I’ve known for 9 years was just laid off.

She’s on H-1B and has 55 days to find a new role or leave the country.

The job market right now is brutal. Layoffs everywhere. Hiring freezes. Hundreds of applicants for every role. And for people on work visas, this isn’t just stressful, it’s existential.

I’m sharing this on behalf of Bhavana Gurram

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐬𝐡𝐞’𝐬 𝐝𝐨𝐧𝐞:
↳ Software Developer at Paycom - built tax filing systems across all 50 U.S. states and 4 international regions
↳ Reduced manual effort by 67% through automation during peak filing periods
↳ Previously at Deloitte - created automation saving 300+ hours/month and built notification systems handling 400k+ messages monthly
↳ Master’s in CS from UT Dallas (3.91 GPA)
↳ Bachelor’s from NIT India - cleared JEE Mains (top 0.01%)

𝐓𝐞𝐜𝐡 𝐬𝐭𝐚𝐜𝐤: Java, C#, Python, React, Spring Boot, .NET, MySQL

She’s looking for Full Stack Developer or Software Developer roles.

I’ve known Bhavana for almost a decade. She’s not just talented, she’s the kind of person who makes teams better.

If this resonates and you’re in a position to help, please reach out or pass this along.

And if not, even a simple like, comment, or repost helps more than you think.

𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐝𝐞𝐭𝐚𝐢𝐥𝐬:
↳ LinkedIn: https://lnkd.in/dqmAsrHw
↳ Email: bhavanagurram14@gmail.com
↳ Portfolio: https://lnkd.in/d5r_3SBe

55 days.
Let’s help her find the right team.

♻️ Please repost, it could truly change everything.

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

Resume

If you're preparing for a data analyst/data science role (in 1 month or less), you must read these case studies!


1. 𝐍𝐞𝐭𝐟𝐥𝐢𝐱'𝐬 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
→ Run 1000+ experiments simultaneously
→ Make data-driven decisions for 230M+ subscribers
→ Statistical rigor with automated analysis pipelines

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dsBsHNqT

2. 𝐏𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭'𝐬 𝐕𝐢𝐬𝐮𝐚𝐥 𝐒𝐞𝐚𝐫𝐜𝐡 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧
→ Built SearchSage to map queries directly to their PinSage embeddings
→ Processes 250M+ visual searches monthly with sub-second latency
→ Increased engagement by 42% for related searches

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dFxS_RWh

3. 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧'𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞
→ Powers 800M+ member profiles with Pro-ML platform
→ Frame feature store ensures consistency across training & serving
→ Photon-Connect automates advanced AI model creation at scale

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d27PeXEY

4. 𝐒𝐥𝐚𝐜𝐤'𝐬 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
→ Handles 2B+ messages daily across millions of channels
→ Achieves <500ms global delivery using stateful Channel Servers
→ Scales to 16M channels per host with consistent hashing

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d8ZYqFzX

5. 𝐋𝐲𝐟𝐭'𝐬 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐢𝐜𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
→ Uses online reinforcement learning for real-time price optimization
→ Balances supply/demand while maintaining service reliability
→ Improved ETAs by 25% through marketplace optimization

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/deienatS

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧:

• What metrics and KPIs drove business decisions
• How they handled data quality and governance
• Statistical methods and ML algorithms chosen
• Tools and tech stack for specific use cases

Don't get distracted by fancy algorithms; start with the business problem.

Most successful analytics projects solved core issues with clear metrics and thoughtful experimentation.

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

♻️ Save it for later or share it with someone who might find it helpful!

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

16 Data Analytics Concepts Every Analyst Should Know in 2026


If you want to break into data analytics (or level up this year), learn these concepts:

𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬:
1 SQL Joins and Window Functions
↳ https://lnkd.in/dapaFjWv

2 Data Cleaning with Python (Pandas)
↳ https://lnkd.in/dzzzFBvM

3 Exploratory Data Analysis (EDA)
↳ https://lnkd.in/dPhTBNm3

4 Statistical Significance & Hypothesis Testing
↳ https://lnkd.in/d-nbprUd

𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠:
5 Dashboard Design Principles
↳ https://lnkd.in/dAG2iAFj

6 Data Storytelling for Stakeholders
↳ https://lnkd.in/dXa6BmtU

7 Choosing the Right Chart Type
↳ https://lnkd.in/dWqGE9ed

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐦𝐩𝐚𝐜𝐭:
8 Defining KPIs and Metrics That Matter
↳ https://lnkd.in/dY85xhkB

9 Cohort Analysis
↳ https://lnkd.in/dqcPRH-v

10 Funnel Analysis
↳ https://lnkd.in/d2qgN2rm

11 A/B Testing & Experimentation
↳ https://lnkd.in/di4p3EKg

𝐌𝐨𝐝𝐞𝐫𝐧 𝐓𝐨𝐨𝐥𝐬 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞:
12 dbt for Data Transformation
↳ https://lnkd.in/d7bszA99

13 Cloud Data Warehouses (Snowflake, BigQuery)
↳ https://lnkd.in/dh3A-QPG

14 Version Control with Git
↳ https://lnkd.in/dGucMpYm

𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:
15 Prompt Engineering for Data Analysis
↳ https://lnkd.in/df6pbWxB

16 Using LLMs as a Thinking Partner
↳ https://lnkd.in/dKW_vgMt

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
SQL and Python are still non-negotiable. But in 2026, the analysts getting hired know how to combine technical skills with AI tools and business thinking.

Master the fundamentals. Then stack the modern skills on top.

What else should make this list? 👇

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

♻️ Repost to help someone starting their data analytics journey

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

These 10 SQL optimizations will make your queries 10x faster.

(Save this!)

1️⃣ Use SELECT specific columns, not SELECT *
↳ Fetching only what you need reduces data transfer and memory usage

2️⃣ Add indexes on frequently queried columns
↳ Indexes speed up WHERE, JOIN, and ORDER BY operations

3️⃣ Avoid using functions on indexed columns in WHERE
↳ WHERE YEAR(date) = 2024 ❌
↳ WHERE date >= '2024-01-01' AND date < '2025-01-01' ✅

4️⃣ Use EXISTS instead of IN for subqueries
↳ EXISTS stops searching once it finds a match - more efficient for large datasets

5️⃣ Limit results with LIMIT/TOP
↳ Essential for pagination and preview queries

6️⃣ Use JOIN instead of subqueries
↳ JOINs are typically faster and easier for the optimizer to process

7️⃣ Filter early with WHERE before JOINs
↳ Reduce dataset size before joining = faster execution

8️⃣ Avoid SELECT DISTINCT when possible
↳ DISTINCT requires sorting and deduplication - use GROUP BY or fix your data model instead

9️⃣ Use UNION ALL instead of UNION
↳ UNION removes duplicates (expensive). UNION ALL keeps all rows (faster) when duplicates don't matter

🔟 Analyze query execution plans
↳ Use EXPLAIN/EXPLAIN ANALYZE to identify bottlenecks
↳ Look for table scans, missing indexes, and expensive operations

𝐏𝐫𝐨 𝐭𝐢𝐩: The biggest mistake I see? Analysts writing queries that work but never checking how they perform. Always test with real data volumes.

The infographic above breaks down each tip visually. 👆

𝐏𝐫𝐞𝐩𝐚𝐫𝐢𝐧𝐠 𝐟𝐨𝐫 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬?

I co-authored a Data Analyst Interview Prep Book with 𝐏𝐫𝐢𝐭𝐞𝐬𝐡 and 𝐀𝐦𝐧𝐞𝐲 covering SQL, Python, Stats, Case Studies, and more.

📚 𝐆𝐫𝐚𝐛 𝐢𝐭 𝐡𝐞𝐫𝐞:  https://lnkd.in/dyzXwfVp

Which optimization tip has made the biggest difference in your work? 👇

♻️ Repost to help someone prepping for interviews

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

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

Stop applying to jobs manually in 2026. 

These 5 websites will save you hours every week. 

(Save this!)

𝟏. JobCopilot
↳ Auto-applies to dozens of jobs per day while you sleep 
↳ Scans 500,000+ company career pages daily for new openings 
→ https://lnkd.in/dEVsCgSA

𝟐. Jobright.ai
↳ AI matches you to jobs based on your skills, not just titles 
↳ According to JobRight, users report 2x more interviews with tailored resume suggestions 
→ https://lnkd.in/dBB53Wq4

3. Teal 
↳ Scores your resume against job descriptions with keyword matching 
↳ Built-in job tracker + 40 email templates for follow-ups 
→ https://lnkd.in/d-gM8gTv

4. Simplify
↳ One-click autofill for job applications (free Chrome extension) 
↳ 200M+ applications submitted, and candidates reportedly hear back more often 
→ https://simplify.jobs

𝟓. Hunter.io
↳ Find recruiter and hiring manager emails for direct outreach 
↳ Free tier available with 25 searches per month 
→ https://hunter.io

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬: 
→ Use JobCoPilot or JobRight for volume 
→ Use Teal to tailor your resume for roles you really want 
→ Use Hunter/Apollo to reach out directly when you find your dream job

𝐏𝐫𝐨 𝐓𝐢𝐩: Don't rely on just one tool. Stack them. Auto-apply for volume, tailor for quality, and reach out directly to stand out.

The job market is brutal right now. Work smarter, not harder.

Which of these have you tried? Any others I should add? 👇

♻️ Repost to help someone stuck in application hell

Nobody tells you that job searching is a full-time job.

I learned that after 1,300 applications.

I was an immigrant. Fresh out of school. No US experience. Pivoting from electrical engineering to data science. In a brutal market.

Most applications went into a black hole. Ghosted by recruiters. Rejected by ATS systems before a human ever saw my resume.

I didn't know how to network properly. Didn't know what hiring managers actually looked for. Didn't know how to tell my story in a way that made sense.

I figured it out eventually. But it took way longer than it should have.

If you're in the middle of a job search right now, you don't have to go through that alone.

Leland is hosting a free event next week:

𝐉𝐨𝐛 𝐒𝐞𝐚𝐫𝐜𝐡 𝐖𝐞𝐞𝐤 (Feb 23-26)

↳ 8+ live sessions on everything from resumes to salary negotiation
↳ Networking strategies that actually work
↳ Interview prep from people who've been on the other side
↳ Led by former hiring managers from Apple, Google, and Coinbase

It's free. And it's built for people who are serious about landing their next role.

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞 → https://lnkd.in/d2p6FdKm

How many applications deep are you right now? 👇

♻️ Repost to help someone who's stuck in the job search grind.

If you're not using AI tools as a data analyst in 2026, someone who is will take your job.

Here are 7 ways to future-proof your data analyst career:

1️⃣ 𝐖𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬, 𝐝𝐨𝐧'𝐭 𝐟𝐢𝐠𝐡𝐭 𝐭𝐡𝐞𝐦
↳ Copilot, Claude, Codex. Learn to use them well.
↳ Make a plan first, guide the tool, check the output.
↳ They amplify your output, freeing you for deeper analysis.

2️⃣ 𝐒𝐐𝐋 𝐚𝐥𝐨𝐧𝐞 𝐰𝐨𝐧'𝐭 𝐜𝐮𝐭 𝐢𝐭 𝐚𝐧𝐲𝐦𝐨𝐫𝐞
↳ Pick up Python. Learn some ML basics.
↳ Understand how pipelines work.
↳ The role is growing; ensure your skills grow faster.

3️⃣ 𝐁𝐞 𝐭𝐡𝐞 𝐩𝐞𝐫𝐬𝐨𝐧 𝐰𝐡𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐬 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬
↳ Tools can crunch numbers.
↳ You're the one who knows what those numbers mean for the team, the customer, the revenue.
↳ That's where your irreplaceable value lies.

4️⃣ 𝐒𝐨𝐥𝐯𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐭𝐚𝐬𝐤𝐬
↳ Nobody cares if you used Spark or Pandas.
↳ They care if you found the issue, fixed the leak, saved the money.
↳ Focus on outcomes.

5️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐫𝐮𝐧 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐲𝐨𝐮
↳ Dashboards are fine.
↳ Automated alerts, self-updating reports, scheduled pipelines? Better.
↳ Make your work do work while you sleep.

6️⃣ 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐥𝐢𝐤𝐞 𝐲𝐨𝐮'𝐫𝐞 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐦𝐚𝐧𝐚𝐠𝐞𝐫'𝐬 𝐦𝐚𝐧𝐚𝐠𝐞𝐫
↳ Clear beats clever.
↳ If you can't explain what the data says in plain language, the insight dies in your notebook.

7️⃣ 𝐒𝐡𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐰𝐨𝐫𝐤 𝐩𝐮𝐛𝐥𝐢𝐜𝐥𝐲
↳ Post what you're learning. Talk about projects. Help others.
↳ This builds trust, opens doors, and puts you on people's radar before you even apply.

The analysts who truly thrive will be more than just SQL experts. They'll be problem-solvers who deeply understand the business, communicate clearly, and adapt rapidly to new tools and challenges.

Which of these are you focusing on right now? 👇

♻️ Repost to help a data analyst in your network.

It was great collaborating with Anas Riad on this one 🙌

You can learn data analytics for FREE this week!

DataCamp just made all 600+ courses in SQL, Python, Power BI, and more, FREE for one week.

This doesn't happen often. Don't miss it.

I've been using DataCamp for years. What I like: their skill tracks actually align with what hiring managers look for, not outdated theory that collects dust. I've gone through a lot of their content.

Here are my favorites if you're learning SQL, Python, or Power BI:

𝐒𝐐𝐋:
→ SQL Fundamentals Track: https://lnkd.in/dtFYQcG6
→ Data Analyst in SQL Career Track: https://lnkd.in/dNRzrWwk

𝐏𝐨𝐰𝐞𝐫 𝐁𝐈:
→ Power BI Fundamentals Track: https://lnkd.in/dCFAxii7
→ Data Analyst With Power BI Career Track: https://lnkd.in/dM6C5YaB

𝐏𝐲𝐭𝐡𝐨𝐧:
→ Python Data Fundamentals Track: https://lnkd.in/d5pd6E5f
→ Data Analyst with Python Career Track: https://lnkd.in/dBSSMdC3

𝐁𝐨𝐧𝐮𝐬:
→ Projects to practice: https://lnkd.in/dgEiZE7A
→ Browse all Free Week courses: https://lnkd.in/dKmMi5Ds

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
SQL and Python are non-negotiable for data roles. Power BI is a huge plus if you're going into analytics.

Start with SQL if you're new. It's the foundation of everything.

Save this post. Share it with someone who's learning.

Paid partnership with DataCamp.

♻️ Repost to help someone break into data

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

Rejected. 

Rejected. 

Rejected. 

Hired.

(You only need one Yes.)

I've talked to hundreds of job seekers in the data space. The ones who land roles aren't the ones with zero rejections. They're the ones who kept going anyway.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐈'𝐯𝐞 𝐬𝐞𝐞𝐧 𝐰𝐨𝐫𝐤:

↳ They stopped taking rejections personally
↳ They used each "No" to refine their approach
↳ They focused on networking, not just applying
↳ They kept showing up when it felt pointless

And honestly? I still get rejection emails for jobs I applied to back in January 2023. The audacity. 😅

Rejections don't measure your worth. They measure timing, competition, and sometimes just bad luck.

The people who get hired aren't luckier. They just didn't quit before their Yes showed up.

Keep applying. Keep learning. Keep networking.

Your offer is coming.

How many rejections deep are you right now? Drop the number 👇

♻️ Repost to remind someone that their Yes is still out there.

This AI workflow replaced 5 tabs and 10 hours of my week.

(And it's simpler than you think)

Most people are still doing this:
→ Brainstorm ideas in Claude
→ Copy notes to Google Slides
→ Rebuild the deck from scratch
→ Go back to Claude to refine thinking
→ Repeat until frustrated

I was doing the same thing.

Then I discovered Claude + Gamma connectors.

𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬:
→ Think and brainstorm directly in Claude
→ Generate a Gamma presentation inside the same conversation
→ Refine your thinking, and the deck updates with it
→ No copy-paste. No context switching. No rebuilding.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚𝐧𝐬 𝐟𝐨𝐫 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬:
↳ Stakeholder reports that used to take 3 hours? Now under an hour.
↳ Weekly updates? Done in one conversation.
↳ Project pitches? Built while you're still thinking through them.

𝐏𝐫𝐨 𝐓𝐢𝐩: Connect 𝐆𝐦𝐚𝐢𝐥 or 𝐍𝐨𝐭𝐢𝐨𝐧 to 𝐂𝐥𝐚𝐮𝐝𝐞 too. Pull data directly into your presentation without leaving the chat.

Your deck finally keeps up with your ideas.

Want to set this up yourself?

Comment "𝐖𝐎𝐑𝐊𝐅𝐋𝐎𝐖" and I'll send you my connector setup guide.

♻️ Repost if someone in your network could use this

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

Save this post to save months of job search!

(Not Kidding)

Job searching can feel like a second unpaid job.

↳ Dozens of tabs open.
↳ Copy-pasting your resume.
↳ Hoping for replies that never come.

But it doesn't have to be that way anymore.

I've been testing a tool that honestly feels like cheating (the good kind): JobCopilot.

It's like having a 24/7 job-hunting assistant that never sleeps.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭:

↳ Searches through 500,000+ company websites for roles that fit you
↳ Automatically fills in repetitive application details
↳ Submits your applications (or saves them for later review)
↳ Sends you a summary of everything done while you were living your life
↳ Lets you manage multiple copilots for different resumes, roles, or countries
↳ Learns what you like over time and gets sharper with every click
↳ Automatically finds emails and LinkedIn of Hiring Managers (New Feature)

𝐒𝐭𝐚𝐫𝐭 𝐮𝐬𝐢𝐧𝐠 𝐉𝐨𝐛𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/dEVsCgSA

If you're stuck in the endless loop of applying, waiting, and hearing nothing, this tool might be your reset button.

I've seen job seekers reclaim their time and land interviews faster because JobCopilot takes care of the heavy lifting behind the scenes.

𝐋𝐞𝐚𝐯𝐢𝐧𝐠 𝐬𝐩𝐚𝐜𝐞 𝐟𝐨𝐫 𝐰𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐦𝐨𝐯𝐞𝐬 𝐭𝐡𝐞 𝐧𝐞𝐞𝐝𝐥𝐞:

↳ Networking with intent
↳ Practicing for interviews
↳ Having those coffee chats that turn into referrals

Your next offer could be sitting in one of those auto-filled applications.

What's the most frustrating part of job searching for you? 👇

♻️ Repost this to help someone who's stuck refreshing their inbox.

90% of ML projects never make it to production. Here's the 8-step framework that works.

𝐒𝐭𝐞𝐩 𝟏: 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐫𝐨𝐛𝐥𝐞𝐦
↳ Start with WHY, not HOW
↳ Is ML even the right solution?
↳ Define success criteria upfront

𝐒𝐭𝐞𝐩 𝟐: 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧
↳ Check data quality: missing values, duplicates, outliers
↳ EDA: distributions, correlations, patterns
↳ Document your data sources and limitations

𝐒𝐭𝐞𝐩 𝟑: 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
↳ Handle missing values (imputation, dropping)
↳ Encode categorical variables
↳ Create new features from domain knowledge
↳ This alone can improve performance by 20-30%

𝐒𝐭𝐞𝐩 𝟒: 𝐓𝐫𝐚𝐢𝐧-𝐓𝐞𝐬𝐭 𝐒𝐩𝐥𝐢𝐭 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧
↳ Split: 70% train, 15% validation, 15% test
↳ Use stratified split for imbalanced data
↳ Never touch test data until final evaluation

𝐒𝐭𝐞𝐩 𝟓: 𝐌𝐨𝐝𝐞𝐥 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠
↳ Start simple (logistic regression, decision tree)
↳ Try XGBoost, LightGBM, Random Forest
↳ Track experiments with MLflow or W&B

𝐒𝐭𝐞𝐩 𝟔: 𝐌𝐨𝐝𝐞𝐥 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧
↳ Use appropriate metrics (F1, ROC-AUC, RMSE)
↳ Analyze errors: confusion matrix, feature importance
↳ Does 85% accuracy actually solve the business problem?

𝐒𝐭𝐞𝐩 𝟕: 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭
↳ Build API endpoint (FastAPI, Flask)
↳ Containerize with Docker
↳ Deploy to cloud (AWS, GCP, Azure)

𝐒𝐭𝐞𝐩 𝟖: 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞
↳ Track prediction accuracy over time
↳ Monitor for data drift and concept drift
↳ Retrain periodically with fresh data

𝐂𝐨𝐦𝐦𝐨𝐧 𝐏𝐢𝐭𝐟𝐚𝐥𝐥𝐬 𝐭𝐨 𝐀𝐯𝐨𝐢𝐝:
❌ Data leakage (using future info to predict past)
❌ Ignoring class imbalance
❌ Deploying without monitoring
❌ Optimizing metrics without business context

𝐏𝐫𝐨 𝐭𝐢𝐩: Your first end-to-end project will be messy, that's normal. Focus on completing the full cycle, then iterate.

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐋?

Here are 5 resources I recommend:
1. Machine Learning by Andrew Ng - https://lnkd.in/diqSeD-k
2. Codebasics ML Playlist - https://lnkd.in/dBiYAeN7
3. Krish Naik ML Playlist - https://lnkd.in/dcpAS5gA
4. StatQuest with Joshua Starmer - https://lnkd.in/dhZ3aVhf
5. Sentdex ML Tutorials - https://lnkd.in/dCFPtDv8

Which step do you find most challenging? 👇

♻️ Repost to help someone starting their ML journey

Building an End to End Machine Learning Project

I just analyzed 2 months of my Instagram data in 45 minutes. 5 insights I wish I knew sooner.

No SQL. No Python. Just plain English questions.

It's an open-source AI analyst system built on Claude Code - 18 specialized agents, 39 skills, all markdown. 

You connect your data, ask a business question, and it frames it, explores your data, finds the root cause, builds a narrative, and hands you a branded slide deck with speaker notes.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐟𝐨𝐮𝐧𝐝:

1. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐑𝐞𝐞𝐥𝐬 = 𝐦𝐲 𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐞𝐧𝐠𝐢𝐧𝐞
↳ 8% average engagement rate (highest on my account)
↳ 406 new followers per post

2. 𝐉𝐨𝐛 𝐒𝐞𝐚𝐫𝐜𝐡 𝐑𝐞𝐞𝐥𝐬 = 𝐦𝐲 𝐯𝐢𝐫𝐚𝐥 𝐫𝐞𝐚𝐜𝐡 𝐞𝐧𝐠𝐢𝐧𝐞
↳ 187K average views per post
↳ 3x more reach than anything else
↳ One post hit 700K views

3. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐒𝐭𝐨𝐫𝐲 𝐂𝐚𝐫𝐨𝐮𝐬𝐞𝐥𝐬 = 𝐜𝐮𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲
↳ 44 saves per post (1/40th of Job Search content)
↳ Not worth the production time

4. 𝐍𝐞𝐯𝐞𝐫 𝐩𝐨𝐬𝐭 𝐨𝐧 𝐓𝐡𝐮𝐫𝐬𝐝𝐚𝐲𝐬
↳ 27K reach on Thursdays vs 140K on Wednesdays
↳ Same content. Completely different results.

5. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐒𝐭𝐨𝐫𝐲 𝐑𝐞𝐞𝐥𝐬 = 𝐔𝐒 𝐚𝐮𝐝𝐢𝐞𝐧𝐜𝐞 𝐦𝐚𝐠𝐧𝐞𝐭
↳ 56% US viewers on average
↳ Critical for brand deals and course sales

𝐒𝐦𝐚𝐥𝐥 𝐬𝐚𝐦𝐩𝐥𝐞 𝐜𝐚𝐯𝐞𝐚𝐭: Two months of data means these are directional signals, not statistically proven conclusions. But the patterns are clear enough to act on.

This kind of analysis would have taken me days. It took 45 minutes.

My content team (Muskan Agarwal, Cherry Media) is already using this report to plan next month's content.

Thanks for building this, Shane, Sravya, and Hai. They're also running a free session tomorrow on analyzing product data with Claude Code. 

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://lnkd.in/d5WX3Cwz
𝐅𝐮𝐥𝐥 𝟓-𝐰𝐞𝐞𝐤 𝐈𝐧-𝐃𝐞𝐩𝐭𝐡 𝐜𝐨𝐮𝐫𝐬𝐞: https://lnkd.in/d9puN3rP

(Repo link and Full report in the comments section 👇)

What would you analyze first if you had this kind of tool? 

♻️ Repost to help a creator or analyst drowning in data

taplio

The Battle

Aishwarya Srinivasan

Venkata Naga Sai Kumar Bysani

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Performance ScoreAishwarya Srinivasan's LinkedIn Performance

Performance ScoreVenkata Naga Sai Kumar Bysani's LinkedIn Performance

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taplio

The Battle

Aishwarya Srinivasan

Venkata Naga Sai Kumar Bysani

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Aishwarya Srinivasan's LinkedIn Performance

Performance ScoreVenkata Naga Sai Kumar Bysani's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreAishwarya Srinivasan's LinkedIn Performance