I graduated high school and started university when I was 16 and have begun my scientific journey. My primary focus always has been on understanding and predicting human behavior. I choose to study economics and advance in predictive modeling.
Since my early studentship, I noticed the gap between the fact and the theory. My primary focus has been combining qualitative and quantitative approaches. I recognized the value and importance of the data and have focused on combining my academic studies and business.
My business journey has started with an analyst position. After I focused on the financial market, I decided to use my analytics skill for trading. Regarding one of my clients’ offers, I took the analyst and chief economist position at one of the biggest energy companies. I had experience utilizing data and using it in extensive project management, handling and handling massive data, and pushing limits for acquired predictions. Predictive modeling and database management gave me an idea for my first startup idea, and I built my first startup in Delaware, the US, with my friends. We succeeded in managing and acquired by a defense technologies company. According to the acquisition contract clause, I worked for the company for two years and had worked two more years. During this period, I had opportunity to work on homeland security projects and develop unique and customizing models for specific needs and cases. the After experiences on I have decided to move forward and built another startup, which developed very innovative technology. After I moved to Israel it became more difficult to manage and also, I had started to work for a fintech company in Tel Aviv and meanwhile continued my research for PhD. It was extremely hectic (to be part of a startup in overseas, working in a full-time job and continue PhD research at the same time requires posthuman effort. Most of the days I had to sleep less than five hours in a day) and decided to exit my shares. I had exited my shares in DeepEye and Altagena in 2020; just focused on my position and study. Also, Covid19 had impact on my life just like everyone. Just before Covid19 has started I got 100% scholarship for Practicum Y-Data. Regarding I completed it I decided to take a break. I just focused on dive deep into data science studies and completing my Ph.D. thesis. Currently I’m focusing on submitting my thesis (which tries to develop an explanation on success and failures in technology & innovation via econometric modeling) and advancing in data science. Currently I’m concentrated on my advanced data science bootcamp and thesis study. My primary focus is taking a data science position in an innovative company regarding my bootcamp and continue my journey.
Team Leading & Management Experience:
Team leading requires deep empathy skills. Teams are made up of people and unlike (Classical and NeoClassical economics) mankind is not rational (and there are no homo economicus, people behaviors are also feeling and emotion driven). My adventure has thought to me; people need to be understood. Especially in high-tech industry time and cost pleasure lean on team managers’ shoulders most of the time. I believe for a team leader the worst thing is buried inro scheduled expectations and demands of high managements. Sustainable working process requires functioning teams and functioning teams requires information cycle, constantly advancing and sharing.
During my professional career I had led technical and managerial teams; especially during product development process. DeepEye was a great example for it. I mostly learned, if team is too large it is better to divide it to manageable numbers and people works harder when they feel trust.
Product Development Experience:
I have experienced several product developments processes which is not limited with only development process also includes deployment process until transfer the process to the product manager. The most important part is managing Agile Triangle, which highly requires deep optimization knowledge. Cost, quality and time constrains have trade off and there is always a preference. During DeepEye I have learned importance of wireframe model. AI & Data products development also requires serious data science study. The team, management and all the stakeholders constantly learn from the data. And main job of data scientist is not limited with A/B testing in product development and management process; it is just very small part. I think as a data scientist the most important responsibility of data scientist is developing unique parameters for the product and understanding the team and market needs and constrains. Also; building projections via predictive modeling is really very important.
Project Management Experience:
Unlike product management project management has clear milestones and time limitations. Especially the budget. The only unclear scope is quality which is highly depended on the qualification of the team.
Especially before and after my first startup, I had many projects experience. Especially homeland security projects and customized technology projects taught a lot. I had 5 massive projects, over 40 small projects, 4 product development projects and few customization projects. During all experiences I think the most important thing is studying every detail in advance and matching the exist sources with required sources. If there is a missing link it should be defined and related changes should be done as early as possible. Also, data tells many thigs and time series analysis plays key role in project managements. As a data scientist I always developed predictive models and try to predict failures in advance.
Modeling Experience:
“All models are wrong some are useful” This quote belongs to Dr. George Box and I highly believe in it. This also mean all models have a lifetime and model stainless is really important. The data scientist should understand there is no perfect model. Models are just simple simulations of the real world and the World is much more complex than any models.
I believe for a data scientist modeling is everything and modeling studies requires very deep mathematic, statistics and econometrics knowledge. I think for successful modeling the most important thing is understanding the data. I always believed in many model principles. Being a “model thinker” provides serious advantages. I had deep experiences during building specific models for understanding the key points between cells and the person who has the most impact in terror and organized crimes. Which required network modeling and classification at the same time. This modeling journey was one of the most difficult processes but I have learned many things. Another great experience was building a predictive model during my first startup. We build a massive database for local contractors. The model provided predictive analytics and output for EPC contractors. The result was really great and we had developed it in a next level and EPC contractors started to learn which contractor is the best candidate to replace failures. Modeling is one of my favorite subjects in data science and as a polymath I also believe modeling is one of the most important key skills for scientists.
Analytics Experience:
Analytics is not really simple and it requires really deep EDA. As a data scientist who has started his career as an analyst; I recognized power of Phenomenology. It influences quality of analytics studies. I had experienced analytics in many different positions such as like product analyst, business analyst, database analyst, etc. During the early time I recognized importance of data cleaning and data process in analysis. Synchronizing visualization and metric evaluation must be the most important stage of the analytics. And both requires deep study. True visualization, choosing the true chart for the case and explaining them via true metrics provides conditions for revealing the hidden patterns. The pattern is not very open and simple most of the time and it might not be linear most of the time. I recognized how trends influences customer behavior in my early years.
Startup Experience:
Generally, I really do not like to hear “this is not my job definition” and as a data scientist anything and everything in the company, industry, market and business calls my attention. It was the same when I was working with few people and also thousands. Unlike many people believes the size is not the most important criteria and the differences between the established corporations and startups. The most important difference is the stage. Startups targets a position in the market and have goals. The most important focus is reaching to the goals for startups. Startups works for taking a place in the market. The established corporations already have their spots in the market. They might not be happy with their current positions or they might target higher spots and bigger market capabilities, but in the end of the day they already have a position. This basically explains the differences between startups and the corporations. Startups had to deal more uncertainty. As data scientist who already built two startup and has worked in many international corporations in massive projects; I believe data scientists can change fate of startups.