Stedman Blake Hood

Optimizing for personal growth, fun, and impact
$$V(t) = \underset{t}{max}\bigg\{ \ f(\ growth, \ \ fun, \ \ impact \ ) \bigg\}$$

In short:

Business interests:

  • Product strategy
  • Sales discovery
  • Testing acquisition & distribution channels
  • Debugging people-related issues
  • Scripting to automate the slow stuff

Career

Retriever | Jan 2018 - President
Co-founder, CEO
San Francisco, CA

Retriever puts meetings on your sales reps'​ calendars.

As a sales leader, your job is to fully leverage your in-house sales reps for the highest bandwidth parts of the sales cycle: pitching, demos, and closing. Retriever takes care of the rest.

We give each of our clients a dedicated team of US-based specialists. Our specialists prospect for leads in your target market, and operate as Sales Development Reps: optimizing email copy, scaling email outreach, handling questions and objections, and finally scheduling meetings on your sales reps'​ calendars.

Retriever is focused on helping residential real estate software companies grow. If you're selling to residential real estate agents and brokers, drop us a line. https://www.rtrvr.co

Triplebyte | May 2017 - Jan 2018
Director of Sales & Business Development
San Francisco, CA

I joined Triplebyte as their first sales hire to build out partnerships on the business side.

Triplebyte's mission is to decouple qualification screening from credentials, to enable a more objective, skills-based recruiting process.

Belay Labs | March 2016 - May 2017
Contract web developer & entrepreneur
San Francisco, CA

Over the past year, I've balanced my time between contract web development, prototyping new products and business ideas, and co-founder dating.

It has been a tremendous opportunity for personal growth and learning about new industries, but I'm now ready to start working as an operator on a small team.

202works | Jan 2017 - Apr 2017
Market discovery & cofounder dating
San Francisco, CA

I joined 202works to get to know Jon as a potential co-founder, and to help the company find product-market fit.

Feb - Mar 2017:
  • Through extensive sales discovery calls, I invalidated the initial business model ("Yelp for lobbyists") determining that:
    • Client acquisition would be costly, and require educating prospects. (High CAC)
    • Typical clients would use the product only once. (Low LTV)
    • Prospective clients were by-and-large content with current solutions for finding lobbying expertise.
Apr 2017:
  • Aggressively pursued sales discovery to uncover viable business models:
    • Connected with lawyers, lobbyists, public relations folks, hedge funds, and private equity shops to learn about their needs.
    • Discovered a viable business model: selling aggregated crowdsourced political forecasts to quantitative hedge funds.
    • Determined that we weren't a perfect fit as co-founders.

I met Jon while doing sales discovery for one of my last side projects. I was looking to sell a service to lobbyists, and he was working on 202works, which in Silcon Valley parlance could be explained as "Yelp for lobbyists".

Jon had been a lobbyist in DC for 11 years, and had recently left his role as Partner at a lobbying firm to pursue his vision. I relished learning from him about the process of regulatory and legislative change in Washington.

We immediately clicked: first meeting for coffee on a thursday to discuss my project. By the following monday, we decided to test out working together full-time.

Business Model Version 0

As soon as I dug in to the progress Jon had made on 202works, it became clear that we needed a strategy to build the client side of the marketplace. Lobbyists were clamboring to use our service; their most pressing pain-point was getting new clients.

The client funnel was not so easy. Over the course of our first 6 weeks together, we methodically tested every acquisition channel we could: referrals (through trade associations, lawyers, and DC folks in Jon's network).

Three things became clear: 1) people who know that they need lobbyists are happy finding them through existing channels: law firms, trade associations, and 2nd degree connections in Washington. 2) people who don't know that they need lobbyists would require a lot of education to convert; and that education would be costly. 3) acquisition challenges aside, lobbying didn't lend itself to the standard marketplace model.

The ideal marketplaces are ones where the buyer has a need for a broad range of sellers at a high frequency. e.g.) Thumbtack, where customers repeatedly use the platform to engage new types of contractors.

For lobbying, this isn't necessarily the case. Most lobbying customers have a static need. If a company's operating in in X industry, then a lobbying specialist in X industry will likely be the only one they'll ever need to engage. They'd use the platform once, then churn. Lobbying services aren't something that the average company has frequent and varied use for.

Once we'd exhausted channels for validating the "Yelp for lobbyists" business model, we began to brainstorm other uses for lobbyists.

We had scraped the public filings, to collect over 18 years of work history on more than 56,000 Federal lobbyists. This rich data allowed us to pinpoint the pre-eminent expert on any Federal level issue imaginable.

The question now was how could we build a business model around this network of lobbyists?

Policy Expert Network (à la GLG)

Through a profusion of discovery calls, we began to realize that we could repurpose these lobbyists as "policy experts". Hedge funds and private equity shops had frequent need for one-off calls with policy experts, in order to get quick insight into a particular deal's potential exposure to regulatory or legislative risk.

As we pushed for sales and pilots, we came upon another, even more promising angle.

Crowdsourced Political Forecasts

Hedgies and private equity shops repeatedly lamented: "Look, we just need to know what are the odds of the Border Adjustment Tax passing". "Is the government shutdown going to happen?" "What's the probability that Section 1033 of Dodd Frank gets ripped out?"

While none of our lobbyists-turned-policy experts could predict these political outcomes with any certainty, I recalled a basic insight from my time at the Fed: the easiest way to get a robust forecast is to average a group of low quality forecasts. A sort of Central Limit Theorem applies.

We shopped this idea around and immediately got interest: from small quant hedge funds to Bloomberg LP.

Vision vs. Market

It was time to get ready to service clients. So we hired a lawyer to work for equity, drafting our terms of service, compliance framework, and initial contracts. And we began discussing priorities for the near term.

It became clear that we weren't aligned on priorities. The dfference came down to vision vs. market. Jon was unwavering in his desire to ultimately realize the vision of creating an open platform for lobbyists (the original business model). I was much more skeptical of our ability to execute on that model. We'd validated a completely different product in our sales discovery calls, and I was ready to commit fully to what the market was telling us it wanted. To my mind, market trumps vision.

Throughout our sales discovery, there were times when this "vision vs. market" issue came up in passing. But I ignored those one-off comments. I assumed it was a less fundamental position for Jon than he was making it out to be. I assumed that when push came to shove, we'd both be aligned on doing whatever the market dictated. I ignored this misalignment because I really enjoyed working with Jon, I was passionate and eager to learn more about the lobbying industry, and I wanted it to work out.

We both determined that this would continue to be a protracted sournce of disagreement. Disagreements are a healthy and unavoidable part of any business partnership. Yet such a fundamental rift in our views on the strategic roadmap seemed to me like a red flag. I wanted to at least be aligned on the question of vision vs. market. As a result, we decided it wasn't a perfect co-founder fit, and to discontinue our work together.

In retrospect, I think I could've forced the issue and arrived at the conclusion earlier than I did. Still, we became good friends, and I look forward to supporting him as he pursues his vision.

Quad | Jan 2016 - March 2016
Apprenticing to learn React
San Francisco, CA

I joined Quad to apprentice under their CTO, Mark Miyashita. In exchange for free labor, he taught me enough front end and React to be dangerous. I'd recommend this route to anyone too experienced for a coding bootcamp, but looking for more real-world experience to hone their craft.

Up until 2016, while I'd done plenty of scripting and programming econometric models, I was unfamiliar with front end web development. I wanted to get to a point where I could:

1. support myself as a contract web developer
2. build MVPs to validate new business ideas

PlateJoy | Feb 2014 - Oct 2015
Co-founder, COO
San Francisco, CA

I left the Fed to join PlateJoy, where I became co-founder & COO. We built a personalized, subscription-based grocery delivery service.

Milestones:
  • Raised a seed round in Spring 2014
  • Went through Y Combinator in Summer 2015
  • Built and scaled our grocery delivery operations, servicing hundreds of customers
  • Scaled to $65K in monthly recurring revenue

I could write a book on everything I learned at PlateJoy. For brevity here's a high-level list:

Business:
  • Structuring teams to maximize ownership & accountability
  • Recruiting, hiring, onboarding, one-on-ones, & firing
  • Pitching Investors
  • Cap table mechanics: calculating convertible note ownership, etc.

Product:
  • Agile product management
  • Data-driven product development using split testing & site analytics
  • User testing
  • Product design

Tech:
  • Scripting to automate marketing and operations processes
  • Simulations to predict demand and streamline logistics
  • Postgres, ActiveRecord
  • Ruby/Rails

Federal Reserve | June 2011 - Jan 2014
Senior Research Assistant
Washington, DC

After gratuating from McGill, I joined a research section in the Division of International Finance at the Federal Reserve in DC.

My goal was to determine whether I wanted to dedicate my life to academic research. Over the course of my time there, I decided that I did not. But I got to build some cool stuff!

I was involved in two main areas of research:

  1. Forecast evaluation and macroeconomic time series modeling:

    • Ericsson, N.R., Hood, S.B., Joutz, F., Sinclair, T., and H. Stekler. Greenbook Forecasts and the Business Cycle. Working paper, 2013.
    • Ericsson, N.R., Hood, S.B., Joutz, F., Sinclair, T., and H. Stekler. Time-varying Bias in the Fed's Greenbook Forecasts. In JSM Proceedings, Business and Economic Statistics Section, American Statistical Association, Alexandria, VA, 2015.
    • Ericsson, N.R., D.F. Hendry, and S.B. Hood. "Milton Friedman as an Empirical Modeler." Milton Friedman: Contributions to Economics and Public Policy. Ed. Cord, B. 2016. 91-142.
    • Ericsson, N.R., D.F. Hendry, and S.B. Hood. Milton Friedman and Data Adjustment, Vox, Forthcoming 2017.

  2. Jump-robust volatility estimation with high-frequency time series:

    TL;DR

    Working under Dobrislav Dobrev, I built a high-frequency financial data pipeline to run through his analytics engine. This enabled us to statistically identify anomalous activity in public markets. Fed policymakers requested that we build this as a tool for macroprudential risk management.

    Our system found an ideal test case in the summer of 2012, when the European Central Bank's president declared "the euro is irreversible". Using a panel of currency futures cross-rates we statistically identified the ensuing market reaction as a purely euro-centric event.

    A bit more on the method of identification:

    Dobri and his PhD advisor Torben Andersen treated volatility as a Lévy process composed of a continuous component and a discontinuous component. With this stochastic framework, they developed "jump robust volatility estimators", which were able to distinguish the continuous component of a volatility series from its discrete counterpart. Their measured were said to be jump robust as compared to traditional Realized Volatility (RV) measure, which simply summed the squared returns on a financial series.

    Distinguishing between these discreta and continuous components is important because often, a large shock can hit the market and cause massive discrete moves. These blow up the RV measure, dwarfing the continuous contribution to overall RV. This in turn makes it harder for analysts to compare the continuous part of a daily volatility estimate, which gives them an idea of overall activity independent of the large discrete moves.

    With these jump-robust volatility meausures, one can construct a t-test of sorts, with respect to RV. $$(RV_{robust} - RV)/\sqrt{ \ quadratic \ volatility }$$

    When the magnitude of this t-like-statistic is sufficiently large, we can statistically identify the presence (and magnitude) of a discontinuous contribution to volatility. i.e. We can detect a jump. Below we discuss a use case in which we can make statistical inference that yeilds immediate qualitiative insight.

    Boosting statistical power with a large panel of currency cross pairs:

    Ok so now that we can statistically detect a jump... detect this!

    Suppose you were to measure this t-test (the jump detector test) across a panel of currency cross pairs. You could construct a diagonal matrix where element [i,j] corresponds to the exchange rate from currency i to currency j.

    What would it mean if a single entire row of this matrix identified jumps, and all other rows detect nothing but continuous volatility?

    For example, consider EUR, USD, JPY. What if we noticed a simultaneous jump in EUR-USD, EUR-JPY, but not the third cross-pair, USD-JPY?

    That would tell us that there had been an "EUR-specific event". Pretty neat qualitative insight, using nothing but a few beefed up t-tests!!

    Now suppose that instead of 3 currency exchange rates you had \(n\)? Then you'd have \({n \choose 2}\) cross pairs. And for a particular currency, \(x\), there are \(n-1\) corresponding cross-pairs.

    You can now use all \(n-1\) t-tests of currency \(x\)'s cross-pairs to test for a jump in that currency. Your statistical power against the null hypothesis of "no \(x\) currency-specific event" thus scales up at the rate \(\sqrt{n}\).

    Super useful for the International Finance Division of the Fed in keeping an eye on international currency markets :)

Education

McGill University | 2007 - 2011
BA, First-class Honours
Economics Honours Concentration
Political Theory Minor
Mathematics Minor

The Honours Economics Concentration was an extremely competitive program, requiring proof-based and graduate-level coursework. Of 220 students who enrolled, only 30 completed the program.

Activities and Societies:
  • President of the Economics Students' Association (ESA), 2010-2011
  • VP of Events of the Economics Students' Association (ESA), 2009-2010
  • International Relations Student Association of McGill (IRSAM), 2009-2011
  • Facilitator Training Program, 2008 - 2010
  • Solin Hall Selection Committee, 2008