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Davy K.M
Worked at Goldman Sachs / JP Morgan
Attended University of Toronto
Lives in Toronto, ON ,Canada
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Davy K.M

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Bank Hackers Stole Millions via Malware(http://nyti.ms/1MooyuA) 
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Davy K.M

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A data scientist is able to collect, analyze, interpret, and solve problems from Data {Content, Social Network , Cloud[IoT], Mobility}
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''Business competition will ensure that robots take over human jobs until 100 percent of industry is automated, from top to bottom,'' Dr. Moravec
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Must See for Big Dreamers (Stanford Graduate school of Business) Leadership:The definition of leadership is delivering results on a sustainable basis, through the team, doing it the right way.
Meritocracy:Competitors can copy your products, but they can't copy your hiring and training or your talent.
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Really nice explanation of Markov Chains: http://bit.ly/UIil68 - love seeing more and more of these interactive explanations!
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Google pioneered this formula, applying it to ad placement, machine translation, spam filtering, YouTube recommendations, and even the self-driving car — creating billions of dollars of value in the process.
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Davy K.M

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Mastering Problem Solving = The Key of Success

I think there is a very strange paradox, when Millennials feel pressured to get higher education for acquiring a job .Then, the constant phrase in most establishments is continuing professional development. While others are saying the opposite and see no benefit in education. How is that? And what about lifelong learning? Education(Self-Learning) will never harm anyone from life experience in this rapidly evolving world. If you can’t continually master how to learn, you will become as extinct as the dinosaur.Which skills should I learn? Which skills do I need to land the job, to impress the client, to prepare for the future?

http://bit.ly/1IJjPPG
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Disruptive technologies explained by +Eric Schmidt :

History has proven that size and past success are no guarantee for the future. Great companies can be surpassed swiftly. Look at Yahoo, Nokia, Microsoft, Blackberry and others who seemed unrivaled just a few years ago, but were disrupted by a new wave of tech companies, Google among them. Many of you are skeptical. I get that. You look at Google, Apple, Facebook, and Amazon and say there’s no way competitors can beat them. I’m less certain.For one thing, these companies are each others’ biggest competitors, because in tech competition isn’t always like-for-like. Many people think our main competition is Bing or Yahoo. But, really, our biggest search competitor is Amazon. People don’t think of Amazon as search, but if you are looking for something to buy, you are more often than not looking for it on Amazon. They are obviously more focused on the commerce side of the equation, but, at their roots, they are answering users’ questions and searches, just as we are.But more important, someone,somewhere in a garage is gunning for us. I know, because not long ago we were in that garage. Change comes from where you least expect it. The telegraph disrupted the postal service. Radio and television shook up the news industry. Airplanes ended the age of ocean liners. The next Google won’t do what Google does, just as Google didn’t do what AOL did. Inventions are always dynamic and the resulting upheavals should make us confident that the future won’t be static. This is the process of innovation.And it’s a process that has been going on since time immemorial, from when someone first had an idea to build something, and someone else thought they could do it better. It’s a process filled with dreamers and doers in equal measure -- people who saw a problem somewhere, and decided to fix it.Innovation is not just about the next whiz-bang gadget, much though people love them. It’s about our quest for knowledge and our humanity
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I just met one of my heroes! Carol Dweck's work on cultivating a growth mindset--a belief that you can learn anything, with hard work--is changing many children's lives. Both +Carol Reiley and I had also enjoyed her book: amzn.to/1xr9CGa 
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It's called The Power of GOD.....
 
Of all the planets we've explored, none have matched the dynamic complexity of our own. Earth is constantly changing, and we're working constantly to explore and understand the planet on scales from local to global. Learn more: http://1.usa.gov/YEnEpj 
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Leadership and disruptive technologies .(Tim Cook should take some examples from Tesla) .... 
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WALL STREET PORTFOLIO MANAGEMENT /REVENUE INTELLIGENCE - AVISO Inc

At First Glance—The 8 Properties of Total Revenue Intelligence Posted by Robert Gersten.

There is perhaps no business process more important than forecasting revenue.

Every critical decision that a company has hinges upon it including the ever so important guidance and expectations that executives set with their Board and Wall Street.

Corporate decisions around hiring and expense levels often change based on the revenue forecast. A lower than planned sales forecast often results in much tighter expense controls and a slowdown in hiring new or replacement employees and a higher than plan sales forecast may result in the opposite situation. Almost all of us have heard about “Revenue Misses” that result in negative perceptions by investors.

Today most enterprises deploy simplistic methods to forecast revenue by analyzing the total sales pipeline, pipeline by sales stage, pipeline coverage ratios, and multiplying it by a fixed probability percent. Unfortunately, typically these rule of thumb methods often lead to substantial actual differences either materially under or over the forecast.

In my experience working the last 25 years in finance and technology executive roles at Rational Software, Hyperion, and Oracle, I’ve learned that most, if not all enterprises want better ways to produce revenue forecasts.

This is the promise of “Total Revenue Intelligence,” a new data-driven approach using advanced mathematics and data science from Wall Street portfolio management to provide appropriate focus on sales and revenue plans, forecasts and projections.

Let’s take a look at the some of the properties of Total Revenue Intelligence:

A portfolio approach to managing a company’s revenue assets across all relevant functions — finance, operations, sales, services, support and renewals
An objective multi-dimensional data driven forecast that provides a risk-adjusted projection and probability to win score across each revenue asset
Real-time comparison of actual and forecasted comps weighed against key company goals and metrics
What if scenarios and simulations across an entire sales pipeline, sales regions, sales teams, and sales reps
Real-time visibility and drill down capabilities into deals moving in or out of the pipeline across any two points in time
Data from multiple sources (CRM, ERP, internal, public) including sales pipeline, sales bookings, billings, revenue and usage
Ability to factor in seasonality to create quantitative and accurate predictions across a company’s revenue data
A user experience that providers executives and domain users with valuable, statistically significant actionable insights

In summary, Total Revenue Intelligence has the potential to change how enterprises make revenue decisions from data and give executives an objective and mathematically proven framework to evaluate risk and return across all revenue streams.
An analytics dashboard for corporate finance teams just received $8 million in financing. The goal? Predicting bad corporate reports before they...
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T. Osinubi's profile photo
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Herman Macon's profile photo
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Work
Skills
Investment Systems Information • Pricing Analysis • Value-Investing Strategies and Analysis • Quantitative Methods for Business Management • Technical Analysis of Financial Markets • Option Trading and Strategies • Capital Markets, Asset Valuation & Portfolio Mgmt. • Corporate Finance and Performance Analysis • Data Management from Enterprise Data Analytics to Data-Based Decision Making • Value Proposition and Technologies of Enterprise Data Analytics.• Machine Learning(Artificial Intelligence) • Programming / Coding=SQL,PYTHON, R and JAVAScript • Asset Management • Quantitative Methods for Business Management • Big Data Technologies • Option Trading and Strategies • Capital Markets, Asset Valuation & Portfolio Mgmt. • Corporate Finance and Financial Performance Analysis Modeling. • Data Management from Enterprise Data Analytics to Data-Based Decision Making. • Value Proposition and Technologies of Enterprise Data Analytics. • business data analysis skills • data analysis to manipulate large amounts of unstructured data.
Employment
  • Goldman Sachs / JP Morgan
    MACHINE LEARNING /COMPUTATIONAL FINANCE / PREDICTIVE ANALYTIC, 2013 - 2014
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Map of the places this user has livedMap of the places this user has livedMap of the places this user has lived
Currently
Toronto, ON ,Canada
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Tagline
DATA.GEEK <- C (“PREDICTION”,”DATA”,”MIND”, “ANALYTIC”,”ALGORITHM”,”INVESTMENT”,”SCIENCE”,”INFORMATION”)….. *Data is The New Gold Of Wall Street*
Introduction
I am a relevant AUTOMATE KNOWLEDGE WORK with many years experience working  in various  projects.My Achievements are DATA ANALYSIS-ARTIFICIAL INTELLIGENCE-INVESTMENT RESEARCH.Also,I’m curious about “new people” ,"self-aware" , and  I trust "my intuition" .Finally,I'm a Long term ASSET not an EXPENSE.However,My passion(Next GOAL) is Investment Science & Intelligence Augmented with a huge focus on Entrepreneurship.


PROBLEM SOLVING / HIGH PERFORMANCE COLLABORATION  / SPECIALIZATION :
 X='Machine Learning' & Y='Artificial Intelligence' 
Investment System Information
• Global Sales Advisor Focusing on Return of Investment (ROI)
• Asset Management/Communications 
• Coding='R,Python,JavaScript,SaS,SQL'
Value-Investing Strategies and Analysis.
Quantitative Methods for Business Management.
Technical Analysis of Financial Markets.
Option Trading and Strategies.
Capital Markets, Asset Valuation & Portfolio Mgmt.
• Corporate Finance and Financial Performance  Analysis Modeling.
Data Management from Enterprise Data Analytics to Data-Based Decision Making.
Value Proposition and Technologies of Enterprise Data Analytics.

My best Reading(Books):
• "The Remains of the Day" by Kazuo Ishiguro
"Sam Walton: Made in America" by Sam Walton
"Memos from the Chairman" by Alan Greenberg
"The Mythical Man-Month" by Frederick P. Brooks, Jr.
"Built to Last: Successful Habits of Visionary Companies" by Jim Collins
"Good to Great: Why Some Companies Make the Leap … and Others Don’t" by Jim Collins
"Creation: Life and How to Make It" by Steve Grand
"The Innovator's Dilemma" by Clayton Christensen
"The Goal: A Process of Ongoing Improvements" by Eliyahu Goldratt .
"Lean Thinking: Banish Waste and Create Wealth in Your Corporation" by James Womanck
"Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should Know" by Mark Jeffery
"The Black Swan" by Nassim Taleb
"Predictably Irrational," by Dan Ariely




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Education
  • University of Toronto
    Finance and Investment Management, 2012 - 2014
    Value-Investing Strategies and Fundamental Analysis/ /Quantitative Methods for Business Management/ /Technical Analysis of Financial Markets/ /Option Trading and Strategies. /Capital Markets, Asset Valuation & Portfolio Mgmt./ Corporate Finance and Performance Analysis/ /Data Management from Enterprise Data Analytics to Data-Based Decision Making/ /Value Proposition and Technologies of Enterprise Data Analytics/
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Gender
Male
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Networking