My journey from ‘Zero to One’

Be relevant. Be fluid. Learn and Innovate. Then, iterate.

“Zero to One” is a phrase popularized by PayPal co-founder and venture capitalist, Peter Thiel, in his book Zero to One. This is said to be the first, and most challenging stage in building a company. In my case, it is Me.

I like to think that building a career is, in many ways, similar to building a startup company wherein I am the founder and I must have a vision of what my career should be like and its impact on the society and workplace. The journey from Zero to One is a time when I have to make sure if my talent and skills are a “good fit” to the current market.

In other words, as a founder of my career I should think if I am relevant to the current economy/businesses? Am I fluid enough to change course when needed? Am I willing to go out of the comfort zone to innovate and, may be, re-start if needed?

These are all important questions to think when we operate in a dynamic, technology-driven environment like current times. Once I’ve found my fit, my journey from “one to 10” will be about doubling down on what is working and continuously iterating on my strategies in order to scale.

My journey so far has been about having a beginner’s mindset and pivoting to stay relevant to my immediate team and overall business. I had no preconceived idea about how my long-term career should look like when I had started but the two metrics that were always intrinsically important to me were:

Am I creating value for my team/business/society?, and,

Am I aligned with the people I work with (in other words, do I like & trust the people I work with)?.

I never had direct answers to the How-s, What-s, Where-s and When-s but I trusted my gut feeling, believed in self-reliance and continuous learning and kept venturing into the unknowns to find my oasis. Continuous growth and development was going to be integral part of whatever I do.

I built my arsenal of academic skills in computer science engineering and then with MBA in decision science. After the high school, I had only two choices - Medicine or Engineering (yes, I’m Asian!). I have been coding since age 12, loved computers, mathematics and physics and so my choice was clear- computer science engineering (little did I know what I was going to do with it in a long-term!!). It was the year 2000 (Dot-com bubble) and my grandfather wisdom was that computers was a means to survival (read: be relevant to the market). Fast forward, I completed Bachelors and Masters in Computer Science, published research paper in Artificial Intelligence and secured one-year internship to build VOIP software for mobile phones at an early stage startup, incubated in the University of Trento, Italy. As a side gig, I also enrolled in Operations Research and Marketing & Strategy courses because the idea of working in a Corporate was always fascinating to me. I knew I was going to apply these skills at some point although at the moment it was less clear how and when?! On completing the internship, and touring the amazingly beautiful Italy, I started working in a Series A startup company in India. These two years were enough to spark my interest in pursuing more than just a degree in computer science. I wanted to witness how technology makes real money ($$)!!

I came to the USA to pursue MBA in Decision Science, finally pursuing my interest in Mathematics, Analytics and Strategy. I wasn’t very sure how to best leverage Computer engineering with Analytics because back in the day, “Machine Learning” wasn’t mainstream and digital transformation was only getting started. Knowing that I could always fall back on my software engineering career if things go down south, I followed my gut and started navigating the unknowns of working in different business segments, hoping that I will be able to thrive eventually. All of this in a foreign land, with people from all backgrounds and languages.

I got my first job as research analyst for mobile device tracker and number crunching was my bread and butter. I felt like I was going to “crush it” given all the academic knowledge about mobile platforms, operating systems, wireless connectivity, network bands, etc. and cool data modeling techniques I learnt during MBA. I realized it wasn’t as straight forward, after all!

First reality hit: Academic based cool projects are good only to get you that first job. In real-life, you start backwards from customer. If your company is selling data products, your customer should be able to derive top key takeaways in first 30sec of looking at the report. And by the way, sometimes simple solution is all you need in businesses.

(More about key takeaways from my data analytics career so far in the next post.)

Well, I adapted. I prepared some of the very basic market share reports that customers needed. I worked on surveys and crossing the dataset against mobile shipment data to get useful insights- all in excel using vlookups, SQL and other functions. My customers were the top mobile device vendors in the industry. They were happy with reports, and my team was satisfied. However, I kept looking for opportunities that allowed for innovation and make a bigger impact.

In one of my projects, report generation was manual and would take 40 man-hours to complete on quarterly basis. I thought this was a constant source of labor work for me that could be avoided with a some VBA automation. Being a coder, I automated the entire report generation process such that it took 2 hours to complete vs the initial 40. All I could think was, how to enjoy the remaining 4 days in a week that I secured for myself!! :) I eventually let my manager know and he was thrilled. Word went out, and I got a few more projects to show off some cool automation skills, quick data visualizations and modeling in excel. I was recognized as an “Exceptional team member”. It was not long before I came to know that with additional automation and charts, the selling price for the reports was increased by 5% for the customers. Now, this was real money, except that none of it came to my pocket.

I changed my job and started working for a semiconductor company for the C-Suite executives in Corporate Strategy. I was working with Sales and Product teams for building forecast and supply-demand models that aided business planning in the strategy team.

Second reality hit: I was working in a company established in 1988, semiconductors was a thing I knew nothing about because I was still in my middle school when this technology wave in silicon industry started. Yet, I was working in the business planning unit and I was doomed for failure had I not stepped up and adapted.

I started reading, learning, shadowing and talking with people. I had to learn and innovate to survive in this job. From academics, I had learnt semiconductor concepts, embedded memory, and flash. I read latest books on flash memory, dove deep, started publishing bi-weekly industry updates, attended conferences, wrote summaries and participated in cross-functional projects. It was an uphill but I was in a company of good people. It made a huge, huge, huge difference. I was blessed with a manager who had put trust in me before anyone else. I got 1:1 time with cross functional members, mentors, excellent teammates, and loved the growth minded company culture. I eventually built new data models for product planning, sales forecast, automated TAM model, and prepared Tableau dashboards of supply-demand for CxOs. It was a thrilling experience.

Third reality hit: The company got acquired by hard disk drive manufacturer. Also, in the year 2015 AWS released earnings first time. I realized that I was nowhere close to cloud computing, rather with the new M&A, I was further distant from riding the next wave of cloud computing.

I switched job and started working as Product Manager for competitive differentiation in a data management company. The product I was managing was a competition to cloud-native solutions. It felt like I was a step closer to cloud computing domain. Learning about containers, cloud storage, hybrid cloud and disaster recovery was my gig. I was in a constant learner mode. I stepped up to learn about competitive Win/Loss strategies as I was tasked with delivering Sales Enablement Training to Americas new Sales hires each quarter. I worked closely with marketing and helped prepare Sales Playbook for competitive messaging. After gauging the pulse from field sales and a number of Win/Loss stories, I led a primary research project with competitor’s customers and provided insights back to the sales that was eventually recognized to be one of the best research studies that clearly highlighted selling and pricing strategies to win competitive engagements. Overall, it was a great learning experience and an year where I had pivoted from semiconductors to cloud storage & recovery solutions. I was bringing value to the team and helped grow business.

Fourth reality hit: The company went through major re-structuring resulting in a round of major lay off across marketing, sales and product, impacting more than 400 people.

This was a good time to revamp my learning in data analytics and learn latest tools and methods. I learnt python and worked on a Kaggle’s Instacart market basket analysis project. Refreshing the fundamentals on statistics, data wrangling, EDA and visualization was the key. These are now said to be the initial phases for Machine Learning cycle. I had worked on a lot of these concept and models earlier during MBA, but now re-doing it in Python and Jupyter and learning about new terms coined to old practices in data analysis was like the next level of nirvana.

Shortly after, I joined AWS to develop pricing tools and build strategies that helped sales drive customer engagements using data-driven methods. Within the first year, I stepped up to expand my scope of responsibilities and learnt AWS services, product positioning, pricing and differentiation strategy to enable field sales with competitive engagements. I got opportunities to interact cross-functionally, learn internal business processes and interact with AWS customers, helping the business development function. This was the most fulfilling job I’ve had in my career.. I learnt, I adapted, I innovated and contributed. I used my engineering background to gain in-depth knowledge of services like EC2, S3, Redshift, Glue, Athena, Kinesis, Kubernetes, Machine Learning and Sagemaker, to name a few. I developed pricing models to support sales cycle acceleration. This was a perfect intersection that allowed leveraging technical knowledge and business analytics. In essence, I felt like I was able to think big, dive deep, and be customer obsessed .. all this while working alongside the most talented people.

A new enlightenment: I cared for startups, more than I initially thought. Startups have the power to change this world like never before. And AWS is an enabling factory. It is where the rubber hits the road for startups. More about “why I care for startups” in my next post.

I joined Startup team last year and now I’m working on some of the very cool data projects to enable Sales. For now, I am a learner again. I’m learning about the startup space, connecting with domain experts and reading a lot. I am also honing my skills in Machine Learning in order to stay relevant with the latest in data science tools and methods so I can ride the new wave in business analytics and contribute more effectively to the data-driven decision making capabilities of my teams.

Overall, my career progression so far has been a function of technical domain knowledge, analytics and growth mindset. I’ve been a constant learner, trying to remain fluid, swiftly changing course and staying relevant. I try aligning with people, but that becomes mutual at some point. I have iterated this approach numerous times and so far it has worked well. This is only the beginning, and is my journey from “zero to one” where I’m finding the correct “fit” for my skills and assessing continuously if I’m providing value consistently. The journey from “1 to 10” where I can procure a seat in rocket ship and scale upwards is yet to surface. Stay tuned. For now, I’m just focusing on learning and getting ready for what comes next.

I will be writing another post on on key takeaways from my journey in data science in businesses. For now, if you are interested, follow me on twitter to learn about my next journey in startups and machine learning. I’ll also let you know when I’ve found my rocket ship.