Calculation Consulting provides data science leadership and expertise. Led by Dr. Charles Martin, who has over 10 years of experience developing machine learning algorithms for companies like Demand Media, BlackRock, and eBay. Demand Media's machine learning algorithms created a $1 billion company but later collapsed due to overdependence on search traffic and lack of adaptation to Google's algorithm updates. Effective data science requires senior leadership, cross-functional collaboration, experimental methodology, and accountability to generate sustainable long-term revenue rather than just cost savings.

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data science leadership
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charles@calculationconsulting.com

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Data Science Leadership
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charles@caclulationconsulting.com

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Who Are We?
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Dr. Charles H. Martin, PhD
University of Chicago, Chemical Physics
NSF Fellow in Theoretical Chemistry
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Over 10 years experience in applied Machine Learning
Developed ML algos for Demand Media; the first $1B IPO since Google
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Lean Start Ups: Aardvark (acquired by Google), eHow
Wall Street: BlackRock
Fortune 500: Big Pharma, Telecom, eBay, …
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www.calculationconsulting.com
charles@calculationconsulting.com
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4. BackStory: in 2011, Search Changed. Forever.
• first $1B IPO since Google
• Machine Learning based SEO algorithms
• Measure the demand for search, and fulfill it
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data science algorithms created a billion $ company
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Demand Media
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eHow.com

5. BackStory: in 2011, Search Changed. Forever.
• Google adapted (Panda)
• Lack of diversification
• Lack of adaptation
• Stock price never recovered
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algorithms without accountability: DMD or Google?
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IPO
Panda
stock price 2011-2012
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DMD
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6. • first $1B collapse due to Panda ?
• CPC revenues down
• premium online publishers died
collapse
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stock price 2011-2012
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$1B in ad revenue was repriced and reallocated
Problem: Cornering the market on
search induced a market crash
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7. Organic Traffic
Revenue / Margins
Panda-Induced ‘Market Crash’
WebMD traffic up, margins negative
traffic increased, yet revenues tanked
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Panda-Induced ‘Market Crash’
Google CPC dropped just after Panda
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9. a Panda-Induced ‘Market Crash’
Like Algo-Induced Stock Market Crashes
• Black Monday 1987 repriced the implied vol curve (i.e. smile)
• LCTM exploited fixed income arbitrage
• Gaussian-Copula model enabled the housing market crash
• eHow ML algos led to Google Panda
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10. Problem: Data Science is Different
“When analytics are this important,
they need senior management oversight”
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Davenport
Thomas H. Davenport
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Generating sustainable revenue requires
Data Science Leadership and Execution
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11. Problem: Big Data does not,
by itself, yield Big Revenues
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• Hadoop everywhere; ROI lacking
• Hadoop is a cost center
• ROI needs cut across business divisions
• Engineering process is not the scientific process
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Algorithms, not data, generate revenue
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Problem: Algorithmic Accountability
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An asset is an economic resource.
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Anything tangible or intangible that is capable of
being owned or controlled to produce value and
that is held to have positive economic value is
considered an asset.
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algorithms can be valuable assets
(and have unforeseen liabilities)
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13. Demand Algos: Gas Station Analogy
Problem: where to open a gas station ?
Need: good traffic, weak competition
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less competitors
no traffic
sweet spot
great traffic
too many competitors
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all businesses balance supply and demand
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• Cross-functional engineering, product, marketing, finance
• Autonomous: separate from the traditional engineering
product lifecycle. self-organizing and self-managing
• Experimental: form hypothesis, analyze data, make
predictions, run backtests, A/B testing
• Self-sustaining: not a cost center; generates revenue
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Data Science is Different
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15. Managing: Data Science Process
• Acquire Domain Knowledge
• Formulate Hypothesis
• Generate Model(s) from the Data
• Predict Revenue Gains
• Backtest Predictions on your Data
• A/B Test in Production
• Attribute Gains to Model(s)
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acting
solving
framing
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• Systems Thinking: leveraging the inter-relationships
between data, marketing, and the customer
• Knowledge Transfer: mentoring — not training — to
develop both personal mastery and team learning
• Mental Models: create a base of small-scale models for
thinking about how to use your data
• Knowledge Sharing: foster collaboration between
research, engineering, and product to drive revenue
Managing: Learning from Data
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charles@calculationconsulting.com