Microsoft

Currently working as Data Scientist in MSRC (Microsoft Security and Response Center)
- Building anomaly detection model using unsupervised techniques on user or application service behavior and activities based on identities used for login and access of resources.
- Formulating problem statement, gathering project requirements, and defining success criteria, in collaboration with product managers and other stakeholders. Data-> Modeling journey is incomplete without business understanding. 
- Building data pipeline and ETL jobs to bring multiple data sources into integration, and create aggregated views as a part of feature engineering.

Senior Data & Applied Scientist

Great Learning

Conducting the mentored learning sessions for the cohorts of Data Science and Machine Learning certification program from MiT Institute for Data, Systems, and Society (IDSS) in collaboration with great learning. 

Link to the program: https://idss.mit.edu/about-us/

I also started to mentor the post graduate certification program on AI and ML for business applications with UT Austin.

Link: https://www.mygreatlearning.com/pg-program-online-artificial-intelligence-machine-learning

Data Science Mentor

GSDC - Global Skill Development Council

• Member of the technical advisor in Data Science, Machine Learning and Big Data 
• Writing blogs in the data science topics to help share the knowledge with community
• Helping develop syllabus and courseware for various certification programs in data science
• Participating in webinars as speakers at various conferences hosted by GSDC

Member of Technical Advisory Group

ThriveDX (formerly HackerU)

ThriveDx partners with different Universities to run the Data Science Certification and Boot Camp Programs.
- Currently working as a Lead Instructor for their Data Science Program, teaching cohorts from SDSU Data Science & Analytics Program 

Link to the program: https://digitalskills.sdsu.edu/lp/data-science-analytics-bootcamp/

Lead Data Science Instructor

VMware Carbon Black

Data Scientist at Security Business Unit of VMware Carbon Black. 

- Built prevalence-based scoring model that effectively suppresses the noise (false positives alerts) by x% , saving the time and resources for threat analysts and hunters
- Worked on building an unsupervised machine learning model to cluster the command lines, identifying anomalous command lines that can result in malicious behavior (algorithms explored in this project are- Doc2Vec, UMAP,  K-Means, and HDBSCAN)
- Created data transformation pipeline in Apache Airflow to orchestrate and schedule the jobs for batch preprocessing, which leads to effective feature engineering for training ML models
- Designed architecture and threat models, in collaboration with engineering team to productize ML model
- Worked with ML engineers to understand, establish and follow CI/CD pipeline for MLOps

Senior Data Scientist

Worked as a Data Scientist for Azure Core Security Service team under Microsoft Cloud + AI.

- Worked on detection models to find patterns, detect advanced threats, bad actor techniques, anomalous or suspicious activity, and active risks to Azure network and systems
- Conducted end-to-end data analytics and reporting on UDP based DDoS attacks using unsupervised learning methods like clustering and pattern analysis to detect and fix the vulnerabilities like OS baseline configuration, open ports/protocols in network security groups
- Performed exploratory data analysis on internet exposed endpoints, curate the raw data from different sources for machine attribution, and generate reports providing actionable insights
- Created a baseline model of Incidents data to help calculate and optimize mean TTA/ TTM (Time To Acknowledge, and Time To Mitigate) metrics for security incidents, meeting the SLAs on those metrics, and closing gaps based on root causes like ticket routing/transfer issues
- Expanded the knowledge of data science within the team by mentoring data analysts to help them learn/grow in the data science domain, and collaborated with data engineers/developers to create, manage and monitor the data pipeline for the ETL workflow

Data & Applied Scientist

Wells Fargo

Worked on building machine learning model for Credit Card debt collections to predict whether customers will contact and pay once their account is delinquent. This model scores the conditional probability based on historical contacts data of each customer across different communication channels like phone call, email, mobile or desktop app, branch visits, and text data. Completed data ingestion phase- working along with data SMEs from various departments to collect and ingest the training data, sampling the data and defining the base population. Worked with data engineers in the team to build data flow diagram, entity relation diagram; and use those artifacts to streamline the data pipeline and complete data transformation/validation.

Tools & Technologies: Python, Git, SQL, Jupyter notebook, Pyspark, H20 ML, SkLearn, SAS

Data Scientist

NAVWAR

-Collecting software scan data from network and process it to identify and remove vulnerable & non-compliant applications using data analytics and modeling.
-Network traffic data analysis (approx. 10K endpoints streaming gigabytes of data everyday in Research Development & Test Environment): cleaning/sampling the data, creating predictive models to categorize, block and remediate the targeted attacks and threat vectors.
-Work with security operation teams like forensics and incident management to collect required data, and perform analysis to aid their investigations.
-Understand/ follow policies regarding information/cyber security like NIST, FIPS, RMF, FedRamp and others.

Tools & Technologies: Python, PostgreSQL, JIRA collaboration tool, Splunk log analytics, Vulnerability Management System (VMS), Enterprise Mission Assurance Support Service (eMASS), Host Based Security System (HBSS), Assured Compliance Assessment System (ACAS)

Security Data Science

US Army

US Army Veteran

Exon Mobile

I am proudly listing this job on my profile history. My actual journey to Data Scientist in US, never started from the tech itself. Here are the skills I learned, and now transferred to my Professional Tech Career:
- People Management
- Time Management
- Supply chain and Inventory Management
- Customer Service ( handling customer complaints, feedbacks and striving for their Satisfaction)
- Prioritizing daily tasks to run the store efficiently

Convenience Store Manager (*started as a server in Deli)

Nirmal is a Data Scientist who yearns for solving problems that actually impact product strategy and business outcomes. As a self-described realist, he believes that with pragmatism and high-quality data, Data Science can be a very powerful tool.

Nirmal has experience working for both government and private industries. He had worked as Security Data Analyst for Department of Navy, and had also served in US Army. He loves working in the rare intersection between security and data science, where there are ample challenges to tackle.

If you are reading this as an aspiring or early career Data Scientist- this message is for you: I am more than happy to chat and give some career advice, or help in whatever the way I can, if you are trying to break into or transition into Data Science career path. Please feel free to inbox me or email me at: nirmal.budhathoki@gmail.com

If you have stalked me this far, you have to hit that follow button if you have not :)  Jokes a side, I love to share frequent posts on data science, machine learning, career coaching and emotional intelligence. So following me won’t hurt.

Want to have some data science career chat regarding interviews , resume reviews or anything ?  You're welcome to reserve a time:
https://topmate.io/nirmal_budhathoki

Me (all excited) : I got my visa approved for studying in US. 
My mother (neutral emotion):  There is nothing I can offer to teach you or guide you, besides my blessings to wish you for success.

I faintly remember my conversation with my mother when I came back home after my visa interview. I could tell that part of her was happy about my future, and part of her was sad that I would be leaving home. 

Growing up as an oldest sibling of the family in a small town of Nepal, and having both of my parents without strong academic background, I felt an enormous responsibility of doing something different. 

I have seen all the struggles my parents have gone thru, esp. my mother who had drained herself out both physically and mentally to raise her kids. She used her savings to pay my initial tuition when I got my visa. 

When I initially started my college application process, there were many unknowns crossings my mind: what program to chose, what college to apply for, what are the scholarship options, which state would be better for international students, how would I overcome the cultural barrier, and many more .... 

Regardless of how badly my parents wanted to help me, they had no clues about what I was doing. 

Fast forward today, they still have no clues on what I do at Microsoft, however they admit that it must be something great 🤣 

Gotta love how our generation evolves. If I think about it now- we all work hard not for ourselves, but to make our next generation better than ours. 

#randomtopics #microsoft #proudparents

Nirmal Budhathoki

The state wanted parity in the salary, and there you go, companies responded 😂 

Does this mean Netflix currently has a Principal Engineer with that wide of an interval difference (730k) in salary ? 

I dont think there could be any explanation to it. The only thing I can think of is that the job is posted as remote, so may be candidates from other planets 🪐 can apply too, where the cost of living can be high 😂 

Next time I know how to respond when recruiters ask me what is my expected salary range 😃 

#randomtopics #salary #transparency #humor

I took an interview last week, where the candidate was at the 50-50 for hire or no-hire. I went for the hire, giving candidate the benefit of the doubt. I believe we all deserve that one chance. 

Its almost like when ML model predicts 0.5 probabity for a binary classifier.

In hiring:

False negative means we missed to hire a good candidate who could not perform well in interview but would have performed well in the actual job.

False positive means we hired a candidate who performed great on interview but later lacked to deliver on actual work. 

If you think about it, the false negative data is impossible to track from company’s perspective. What’s missed is missed.  

One of the key flaw I noticed was the candidate lacked the structured method of answering. The candidate knew most of the topics but he/she was mixing everything randomly and missed the main points. 

In order to have a clear and structured response, start by clearly understanding the question, then mentally outline your answer, breaking it down. This structure helps you introduce your point, elaborate on it with details or examples, and conclude succinctly.

#datascience #interview

Friday nugget: 

Data Science is more than machine learning. 

Machine learning is more than deep learning. 

Deep learning is more than NLP (natural language processing). 

NLP is more than doing LLMs. 

LLMs is more than doing chatbots.

Use the terms properly. And please do not use AI for everything 😂 

#datascience #jargons #useitproperly

I’ve failed many interviews as an early grad applicant. Even after having some work experience, I’ve failed interviews from companies like Microsoft, Amazon, Meta, Google, and Apple, you name it.

The brighter side for me was about gettting these interviews regardless of the result.

All of these failures have helped me to perform better the next time. I’ve also had multiple offers from some of these companies. 

We only see success celebrated in social media platforms including #linkedin. People hardly share anything about their failures. 

This is a prime example of survivorship bias, where the focus often remains on successes while the vast number of lessons from failures remains hidden.

All I can say is to repeat my mantra- keep up the grind 💪 

#datascience #interviews #failure #survivorship #bias

What is the difference between Data Scientist and ML Engineer ? 

One of the common questions I get in almost all of my mentorship calls. 

To answer these questions effectively, we need define some attributes or factors to differentiate them. For simplicity I have used:

🔵  Role Definition
🔵  How industry uses the title
🔵  Skills Needed
🔵  Job Demand

TL;DR of the attached table: 

Data Scientists analyze and interpret complex data to drive strategic decisions, utilizing skills in statistical analysis, data visualization, and predictive modeling. ML Engineers focus on designing and deploying ML models to solve specific problems, requiring technical knowledge in ML algorithms plus software engineering skills for model deployment/ integration. 

#datascience #datascientist #mlengineer

Data Scientist vs. Data & Applied Scientist ?

These titles are common across Microsoft. Most of the roles are posted as Data & Applied Scientist while you may come across some posted as Data Scientist. 

So whats the difference? 

For the most part, there is a big intersection. It is also done to diversify skillsets within a team. Most of the teams tend to use the later one, DnA Scientist. Oh wow I just coined a new name, where data is in our DnA 🧬 😂 

Based on my own experience, I have captured the comparison of these titles by various attributes. 

Tip: Read the job description entirely to understand what type of role it is- whether focusing on BI and analytics, core ML, Applied ML, or sometimes even the data engineering. 

#datascience #appliedscience #jobtitles

Red bull has always been smart when it comes to their marketing campaign. When they first penetrate the European market, they did something very unique. Over the night, they were dropping the empty cans all over the trash bins in the city. They also targeted the bars to throw the empty cans around. 

It quickly captured the interest among the folks - what is this drink that everyone is drinking? That curiosity led the folks to try it.

Yesterday, during the eclipse they planned an amazing photo shoot, because they wanted social media to talk about it for free, just like what we are doing here 😂 

We do not need AI for everything. Creativity goes beyond AI 😎 

#randomtopics #redbull #marketing #creativity #ai

There is a new parameter added in all sklearn models called magic, which takes boolean values. 

# Creating the Logistic Regression model
model = LogisticRegression()

# Fitting the model with the training data
model.fit(X_train, y_train, magic= True)

This new parameter will magically produce 90% or higher accuracy regardless of what data you pass. 

This will be another game changer feature in the ML world 🌍 

#datascience #sklearn #machinelearning

We are hearing a lot of conversations in AI taking over jobs. 

There has been talks about Devin, the first AI software engineer, taking over our coding. 

I am just waiting for my Devin to take over my jobs. Right now he is only give me extra jobs instead. So far my model is still training with lots of messy code 😆 

He is already popular in the AI world 🌍 😂 


#randomtopics #humor #ai

An old joke with a new twist! 🍉

An old man was selling watermelons with a sign that said 1 for $3 and 3 for $10. A young man bought 3 melons for $9 and told the old man he needs to learn sales business. The old man smiled and said - ya you saved a dollar. In reality the young man ended up buying 3 melons when he only needed one 😃 

We all have heard this story. Now let’s spice 🌶️ it up. 

As people started noticing his trick, and the inflation, his sales suffered. The old man had to change his strategy. He called his watermelons as LLMs - 

Large & Luscious Melons 🍉

After that people didn't even think twice and started buying them crazily. 

Old man smiles: Everyone loves LLMs. 

Happy Weekend !!!

#randomtopics #humor #llms

I was in a meeting with one of my coworkers.  He was in a similar boat like me with two little kids 🧒 👦 

He was watching the baby’s monitor, and he had to excuse himself for few minutes to tap his baby to put him back to sleep. I recalled my own memories. 

Then we both admitted how lucky we are to be part of Microsoft , a company that truely values employee’s family needs. 

Microsoft has not yet mandated the on-site work. Right now, it is a privilege to have the hybrid option, depending on employee’s discretion. 

Every company has its own pros and cons, . Microsoft may not come in par with other tech companies in terms of compensation, but they certainly beat most of them on culture, and work life balance; factors often neglected during offer negotiations, but they matter the most. 

I was helping one of my mentees today, on evaluating her offer, and these are some of the key things I shared.

In future, I will write about the entire hiring process of Microsoft regarding Data Science roles.

#randomtopics #wlb #culture #offer #negotiation #datascience

"If a man is called to be a street sweeper, he should sweep streets even as Michelangelo painted, or Beethoven composed music, or Shakespeare wrote poetry.” 

This is a famous quote from MLK. 

The core message is- regardless of what you do, nothing is big or small; every job matters and you do the best you can. 

I worked in a deli shop making sandwiches during my college days. I knew nothing about it, but I learned. I did my work with a passion, not just for the sake of doing it. People used to drive an extra mile for my sandwich 🥪 

I’ve proudly added that role in my #linkedin profile under experience. There is no shame to it. I have learned many skills that were transferable to my current tech career:

1. customer’s voice matter
2. communication is the key
3. your product will speak for you
4. time management is important 

P.S. The picture added here is about a Chai wala (tea-man) named Dolly from India, who is very popular for his passion and skill of making and serving tea. And ya that’s correct, Bill Gates was his customer 🍵 🫖

#randomtopics #workethics #ambition #goals

I had an opportunity to participate in yet another hackathon event focused on security. One of our projects was the first place winner 🥇 🎉 

For me- teamwork, collaboration and learning were the biggest wins. 

#cybersecurity #hackathon #microsoft

credly.com • 1 min read

Hackathon Winner 2024 was issued by Microsoft Security to Nirmal Budhathoki.

Here is one interview question that I asked to the candidates in one of my recent data science interviews. 

It is very simple; yet it covers some of the basic things on logical thinking of ML problem solving 😊 

Given the following features, the task is to predict house price. How do you approach this problem following the ML workflow ? 

Features (X): 
- square footage
- number of bedrooms
- school rating (1-10) 
- crime rating (low, medium, high) 
- year built
- location

Depending on how the candidate framed the answer, some of my follow up questions were:

1. I found high correlation between sq. footage and no. of bedrooms. How do you handle it ?

2. Can I use the year built as it is or what do you suggest ?

3. Location column has almost 20% data missing. How do you handle it ?

4. Crime rating seems to be categorical feature but with ordinality. Can I simply use one-hot encoding or you suggest something else ?

5. What does the sales price (y) distribution tell you ? 

I will share my expected answers in the next post. But I would love to hear your thoughts on it. If interested, plz add your comments.

#datascience #interview #machinelearning

If you are in data science, and not following the beautiful animated learning videos from Grant Sanderson, then you are missing out. 

His videos were my learning doses during my data science degree. He makes the math fun, and I can listen his voice all day long 😊 

He just released a new video on GPT and LLM, one of the best explanations I have come across. 

Feel free to share the words:

https://lnkd.in/dxukdc44


#datascience #nlp #gpt #llms #3blue1brown

Holi, also known as the Festival of Colors, is a celebrated Hindu festival marking the arrival of spring and the victory of good over evil. 

It's a time for joyous gatherings where participants throw colored powders and water at each other, symbolizing the vibrancy of life with nature's colorful palette. 

Happy Holi !!!

#randomtopics #happyholi

I have been working in a secret project, some people call it stealth mode startup, and today I am announcing the release of GPT - 5 model, which is the early version AGI. 

This morning, with all the excitements I sent my first prompt:

Me: Can you read my mood and make a coffee recipe with the exact match to start my day? 

myGPT-5: Sure, here's the "Data Scientist's Morning Delight"

1. Brew Precision Coffee: Start by treating the water-to-coffee ratio as a hyperparameter. Aim for a model that minimizes bitterness but maximizing flavor. 

2. Normalize Almond Milk: Gently heat almond milk to achieve a Gaussian distribution of warmth, ensuring no gradient descent is too steep for a smooth coffee.

3. Sweeten with Maple Syrup: Add maple a syrup, by applying a regularization technique to prevent the overpowering sweetness from dominating other features.

4. Spices with Dimensionality Reduction: Sprinkle a pinch of nutmeg and a dash of cinnamon, carefully reducing complexity without losing the essence of the spice's flavored profile.

5. Top with whipped cream and caramel drizzle: Finish with a generous layer of whipped cream, symbolizing cloud computing's limitless scalability. The caramel drizzle represents the sweet reward of a well-tuned beverage. 

Enjoy your cup of "Data Scientist's Morning Delight," the perfect drink to sip on while pondering the P-values of life or debugging your latest predictive model.

Happy April 1st  😂 

AGI here means: April’s Greatest Invention 

#datascience #humor

"People with high expectations have low resilience, and unfortunately, the resilience matters."

This was one of the beautiful lines that Jensen Huang spoke on his recent visit to Stanford.

I can recall that in my military career also- resilience was always highlighted as core skill to build. Falling down is inevitable, if you cannot bounce back, that's when the failure gets you. Once you build resilience, failure cannot defy you.  

If you are currently going thru some pain and suffering, it's a good sign for your upcoming success, because it is only helping you build your resilience. 

In a funny way, he said the only thing he could wish for the Stanford graduates is some pain and suffering 😃 

I added that short clip of his video in the comment.

#randomtopics #resilience

Dunning-Kruger Effect: Expertise vs. Confidence

The progression of learning from general to specific fields in artificial intelligence (for example LLMs) can be framed within the context of the Dunning-Kruger Effect as follows:

People learning LLMs directly: They often fall on the left side of the Dunning-Kruger curve, where their initial surge in confidence outweighs their actual expertise. The superficial understanding and use of LLMs leads to an overestimation of knowledge and abilities. 

People who learn deep learning and transformers before LLMs: They fall in the middle of the Dunning-Kruger curve, where they have enough insights to recognize the complexities and challenges of LLMs. This awareness leads them to doubt their capabilities, reflecting a low confidence while actual competence can be higher.

Experts with a background in machine learning, NLP, deep learning, and then learning LLMs: They fall on the right side of the curve, where their extensive and deep knowledge across the related domains enables them to accurately assess their capabilities. Their expertise allows them to understand both the breadth and depth of LLMs, matching their high confidence to high expertise.

Bottomline: Do not try to jump into LLMs directly. Build your foundation. Learning is a journey, not a destination.  

Happy Friday !!!

#datascience #ai #deeplearning #transformers #llms #dunningkruger

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