70 hours work week VS Thinking perspective VS Consulting vs Domain Knowledge

Numbers do not reflect the quality of outcome rather consistency, ideas, and experimentation matters

Steve Jobs on Continuous Process Improvement

• The theory behind the question of why we do ?
• Ways of doing things? Question the basics? Shift to an Optimistic point of view of Relooking options?
• The shift of perspective / optimistic point of view?
• That's the way it's done vs Finding new ways/opportunities

Steve Jobs on Consulting

• Owning/working extended period few years
• See-through actions / accumulate scar tissues
• Learning a fraction vs Not owning results
• Picture of a banana vs 2D vs Experience of doing vs 3D views
• Knowing is a 2D view vs doing is a 3D view (Hands-on Matters)
• Take responsibility for work

This was the key lesson for Why I was able to rewrite the warranty engine in Microsoft for 224 million serial numbers

Team - Teamwork 

"My business model is The Beatles. They were four guys who kept each other's kind of negative tendencies in check. They balanced each other, and the total was greater than the sum of the parts. That's how I see business: Great things in business are never done by one person, they're done by a team of people."

Ideas to Reality

This lies in process / constant innovation

It is compounding value over the years :)

Hire people trustworthy plus have complementary skills. Aligned on vision but diverse in skills

Arrogance - Keep a Tab

The biggest enemy of continuous growth and progress is arrogance.

“All of us need to be on guard against arrogance, which knocks at the door whenever you are successful.” —Steve Jobs pic.twitter.com/NOLL1MLjgl

— Vala Afshar (@ValaAfshar) December 9, 2023

Keep Exploring!!!

Zillow Machine Learning Fallout

Good read - Link

Machine learning is no silver bullet if you do not consider domain, data, changing environmental factors. A classic case of missing domain knowledge is flagged in this story.

• Zillow does Real estate - selling, buying, renting, and financing
• Zillow home value estimation models failed.
• Assumption - assumption that housing prices would continue to climb without interruption at a stable rate
• The domain experts warned of issues with the predictions.
• The business went ahead anyway. Finally, it bombed

Lessons

• Domain expert warnings considered as Go / No-go for production, not just model accuracy
• Learn / Incorporate Data Changes to understand changing trends
• Performing A/B Experiments to understand customer behaviors and leverage optimal values based on outcomes
• Better model/feature management / keep improving on features / incorporate external factors based on domain expert perspectives #machinelearning #technology #datascience #domainknowledge

Another good read Zillow, Prophet, Time Series, & Prices

i cannot get over that the zillow data scientist job posting "strongly prefers" you have experience with a python library that is designed to make it as piss easy as possible for little babies to do y_t = f(t) time trend / curve fitting forecasts. pic.twitter.com/YTUcgascCi

— Senior Data Masseuse (@ryxcommar) November 3, 2021

WHY IS INTERMEDIATING HOUSES SO DIFFICULT? EVIDENCE FROM IBUYERS

• Predict that households’ wiliness to pay for liquidity is highest in those markets
• Sophisticated algorithmic pricing

My Perspectives

• I love the housing.com approach to rank an area based on amenities, wellness, connectivity
• Plus a pricing range based on amenities and facilities provided
• Plus growth potential / Availability
• Demand vs Supply

A combination of this would suggest a recommended price that a domain expert could adjust based on other external factors. ML is a guideline, not a blind predictor

Keep Thinking!!!

My Perspective of Data Science

Data Analysis

• Find #Insights, Turn it into Why-questions
• Seek Surprises sudden #peaks, #lows, Harness it into How-Questions
• Plot data in different dimensions #Month / #Year / #Sales / #Products / #Divisions, Find #Insights in every #perspective

Learn the story behind the numbers!!!

Mindset and Skill
#Skill is not just how fast you solve a problem but how many different #perspectives/ #techniques / #approach you can find to solve the problem

Domain Knowledge
If you don't understand your #domain, you won't understand your #data, you will miss the #insights and your model will not be built based on the domain needs. It is easy to do .fit, .predict but its harder to find the hidden feature variables before building the model. Understand your #data before building your ML use case. #perspective #machinelearning #datascience

General Guideline - How I evaluate data science candidates?

• Different business problems solved and their ML lessons learned, Deep Dive on Implementation, Algo used, Features Evaluated
• Data pipeline set up challenges faced to deploy in production
• How do you keep track of new papers / evaluating and learning different frameworks
• How much do you code on a daily basis for work / personal learning
• Ability to bring different perspective/techniques solving problems
• How Algos works, Learning's with Overfitting, Underfitting, variable selection

#DataScience Solutioning #Lessons Learnt

Step -1 - Understand / Solving a problem from business #perspective is first move

Step -2 - Scaling the solution is next milestone

Step -3 - Hosting it / Porting it is the last milestone

All the steps are important but priority relies on completion of each task in sequence so that we do not go back to fix them again. Hire talent accordingly #machinelearning #vision #perspectives

Analyze -> Solution -> Scale -> Deploy (Talent pool varies accordingly, Get the mix to excel)

The field is evolving on a daily basis. We need passionate, curious learners with an experimentation mindset!!!

Document can be downloaded from link

Happy Learning!!!