About

Work Experience

• Principal Data Scientist, Conviva

  Jun, 2021 - Dec, 2022

  My technical work has focused on improvements to the Stream ID product (community detection). More generally, I've tried to socialize data science best practices and build a more data-driven culture. Scala, python, databricks, SBT, GCP.

  • I designed and built a Scala implementation of Conviva's next generation household id algorithm. This used a more modern, flexible statistical machinery to preprocess and detect communities in unreliably labeled data.
    The prototype was used for an RFP on a production sized dataset that required functionality outside the scope of our production algorithm.

  • I designed and built a framework to enable reproducible research. The idea is that an analysis is typically comprised of multiple tasks that can be described by a DAG, and, in general, each node of the DAG will use a different parameter set. Typically, one wants to build and test the functionality of each node independently, and upstream calculations should be cached when possible. The framework manages these incremental results. It furthermore enables low-overhead, per-task logging through log4j / slf4j. It is designed to be useful for local testing, deployed as a JAR in databricks, and as a databricks Job submission tool through the command line. It centralizes the implementation of common tasks and largely solves the problem of databricks notebook drift. This enabled a shift where databricks is used to prototype an idea that is subsequently properly codified within the framework.

  • I'm currently providing data science support / consulting to a new product that hopes to provide instrumentation as a service beyond the streaming video space.

• Consultant, Inferentialist LLC

  May, 2012 - Jun, 2021

  My consulting company. I provide statistical and data science expertise to clients.

  • capacity planning,

  • promote, design, and deliver controlled experiments (A/B tests) where possible,

  • marketing analyses including engagement ladders,

  • customer retention models and churn analysis,

  • data integrity, data consistency, and data quality assessments.

• Senior Data Scientist, ServiceNow

  Dec, 2016 - Oct, 2017

  I developed, implemented, and tested statistical algorithms for the Operational Intelligence team.

  • Developed a framework for simulating correlated events from (randomly generated) branching processes that included (randomly) censored event data.

  • Developed a novel event correlation algorithm which recovered 98 percent of correlated event pairs in the simulated datasets and generated new insights in unlabeled customer datasets.

  • Implemented a bivariate Kalman Filter with level, trend, and seasonality components and which allowed for missing data; combined this with a novel, 'split-hypothesis' paradigm to detect anomalous jumps in the state space. US Patent 10635565.

• Researcher / Senior Data Scientist, Microsoft

  May, 2014 - Oct, 2015

  I provided statistical support for the O365 Customer Intelligence and Analysis and Bing Experimentation Teams.

  • Extended existing CLI software, and incorporated the new tooling into a web service. This allowed external partner teams to preview the ExP platform while waiting for a more formal migration.

  • Designed and built a generic, extensible data summary library in SCOPE/C#. The library was widely adopted, and the Avocado Team internalized it as a core part of a web-facing, 'deep-dive' toolset.

  • Worked cross-functionally with PMs and developers on the OneNote and Exchange Online teams to enable A/B experimentation on the ExP platform. Designed first-run experiments for these teams.

  • Implemented a change point detection algorithm for support ticket volumes which was used to identify unannounced deployments of new instrumentation. Corroborating these identifications allowed us to improve the documentation of, and communication around, the deployment process. It also allowed us to better clean the data, drastically reducing the variability of statistical estimates.

  • Identified classes of support tickets that were strongly correlated with early tenant lifecycle / onboarding issues.

  • Developed an interpretable forecast model for customer support tickets which informed budget allocations related to future staffing requirements.

• Quantitative Modeler, Zillow

  Jun, 2011 - Feb, 2012

  I worked on fixes for the Zestimate algorithm.

  • Advocated for interpretable home valuation models that incorporated spatial and temporal structures (in contrast to off-the-shelf random forests).

  • Developed a cross-validated, coefficient-of-variation metric to assess the risk of temporal instability in a home's Zestimate history. This indicated that Zestimates with non-physical behavior were far more prevalent than previously thought.

  • Developed an alternative to the ZHVI--Zillow's proprietary home value index--based on estimating discount curves from longitudinal, repeat sales. This provided an improvement in the estimator for small samples.

  • Identified, and removed (post-hoc), 'spikey' Zestimate behavior in a collection of 100 million Zestimate histories. This resulted in 'corrections' to nearly 4 million time-series.

• Machine Learning Intern, Globys

  Oct, 2010 - Jun, 2011

  I built statistical models for up- and cross-sell marketing opportunities for mobile add-on packages.

  • I used an Apriori algorithm to create new features from historical purchases. These attributes had higher predictive power and produced significant lift in our models.

  • "I provided statistical support for implementing a step-wise, logistic regression model in production.

• Consultant, Numerix

  Jun, 2009 - Aug, 2010

  I provided expertise on numerical stability issues arising in the multi-factor backward lattice algorithm.

• Senior Software Developer, Numerix

  Jun, 2006 - Aug, 2007

  I worked on numerical codes for pricing exotic financial derivatives.

  • Reverse engineered a multi-factor, backward-lattice pricing algorithm to diagnose and fix numerical instabilities.

  • Wrote new solvers for calibrating BGM Libor interest-rate models to market data.

  • Implemented a PDE solver to price Asian and Lookback options with discrete observation dates.

• Technical Staff, MIT Lincoln Laboratory

  Sep, 2001 - May, 2002

  Sensor Measurement and Analysis Team

  • Implemented backscatter models and tracking algorithms for RADAR applications.

Projects

• Capacity Planning (xCloud), Analytics

  Jun, 2020

  Introduced back testing into an existing methodology which showed that 13% of presumed-to-be-available resources were never needed.

  Developed a cohort- and market-segmented analysis using a markov chain model designed to capture invitation lag, onset spike, and steady state behaviors. This new model suggests that the existing methodology consistently over-estimated monthly steady state capacity needs by 100% to 300%.

  Deployed calibrated models into Excel to enable real-time, what-if scenarios for the finance team. The excel add-in used the Python xlwings package.

• Measuring Effect Size (xCloud), Analytics

  Apr, 2020

  Platform- and title-level analyses quantifying the effect of xCloud participation on invited-user behavior within the larger Xbox ecosystem. We considered user activity on the console in the 28 days prior to receiving an invitation; and the user activity on both console and xCloud for the 28 days after their first gameplay on xCloud (or synthetic first gameplay).

  Addressed self-selection bias by introducing sample weights for a non-participating, pseudo-control group. The weights were chosen to induce a change of measure that rebalanced the pseudo-control group against a participating, pseudo-treatment group. The rebalancing was with respect to previous console behavior.

  Addressed invitation lag for the pseudo-control group by synthesizing a first gameplay date. This was accomplished by sampling from the distribution of invitation lag conditioned on the date of invitation obtained from the pseudo-treatment group.

  For title-level analyses, developed a family of usage-pattern indicator metrics which provided intuitive estimates of standard business metrics: retention, replacement, extension, new usage, exploration, and conversion.

• Published Pages, Publishing Platform

  https://dlennon.org/pages

  Jul, 2019

  I designed the publishing platform to host this resume and my portfolio. The look and feel was inspired by the jsonresume.org 'elegant' theme. The project depends heavily on the Python Klein library, Jinja2 templating engine, and pandoc to transform Markdown into HTML. The platform also permits easy inclusion of external, templated HTML; page-specific JavaScript; and registered python plugins associated with ajax endpoints on the server.

• Charity Search, Web Application

  Jul, 2019

  I imported non-profit IRS tax returns into ElasticSearch and built a website to search for local charities.

• Multivariate Kalman Filter, Python Package

  Apr, 2019

  I developed a multivariate Kalman filter code for non-stationary time-series analysis.

  • Full multivariate model enabling fast, online analysis;
  • Native support for level, trend, and seasonality components;
  • Non-informative priors for state space initial conditions;
  • Automatically handles missing data;
  • Support for modeling intervention effects

• Twittalytics, Web Application

  Aug, 2018

  The app monitors the Twitter stream and maintains a dynamic list of trending hashtags; and, for each hashtag, a random sample of relevant tweets.

• Carpool Project, Android Application

  http://dlennon.org/carpoolproject

  Feb, 2013

  I prototyped a location tracking app to collect daily commute data with the intention of helping people create viable carpools.

• Interactive Spatial Heatmaps, Web Application

  Dec, 2013

  I built a website to visualize fused geographic (TIGER/Line) and census (ACS,BLS) datasets. This featured an interactive heatmap that reported spatial statistics aggregated at a metro (MSA) level.

Publications

Skills

Education

Interests