Before DataFox, we had jobs in which doing manual data grunt work was a prerequisite for accomplishing our real jobs. We decided to use data science and software to solve this problem.
Our mission is to eliminate grunt work for business professionals.
We started with investors and sales teams who needed company intelligence in their CRM. To help them, we built a database of the world’s most relevant companies, using ML and NLP to capture insights on those companies and infuse that intelligence into our customer workflows. This helps them skip the grunt work and jump straight into strategic decision making.
DataFox is a team of people who value #ConstantLearning. Hover to learn about a memorable learning from each person’s past studies and work experience, as well as favorite thing about the team at DataFox.
School: Discounted Cash Flows and Option Pricing Models at Stanford.
Work: modeling private company growth at Goldman Sachs.
Favorite thing: feel fortunate to work with such a committed, tight-knit team.
School: the expressiveness and complexity of Bayesian modeling
Work: bridging the gaps from static data modeling to a living production system
Favorite thing: learning from the brilliance of each and every one of us
School: How to write clearly, whether in ML code or religious studies essays.
Work: Scaling backend sytems and engineering teams to support tens of millions of users at Box.
Favorite thing: Building a great team and watching them thrive
School: Object-orientation, encapsulation, and inheritance at Stanford
Work: Cross-platform information pipelines
Favorite thing: We live our values
School: For me, calculus is easy compared to statistics, which are really hard
Work: Mapping 300,000 corporate purchases to spend categories
Favorite thing: The blend of professionalism and fun that people bring to the office
School: Startup valuation modeling through an entrepreneurship program
Work: Learning how sales organizations scale at Salesforce
Favorite thing: The team expects a lot from each other but in turn will do anything to help
School: Primary research for competitive intelligence projects
Work: Manually building the training data for Machine Learning models
Favorite thing: Managing more than 250,000 hours of remote team member time
School: Law and economics course on price and incentive theory at Duke
Work: Litigation valuations and fraud investigations at PwC
Favorite thing: Seeing our team grow with professional go-getters
School: Data establishes shared understanding of reality (political economics at Stanford)
Work: Finding best tweets from different perspectives for trending events, Twitter
Favorite thing: dedicated and talented, yet ego-free
School: Number theory
Work: Modeling clients' portfolios based on risk appetite
Favorite thing: Most social group of highly intelligent people around
School: Agent-based modeling in economics
Work: Using financial transactions data to visualize money-laundering networks
Favorite thing: It's easy to ask anyone for help
School: Building an OS from scratch at MIT.
Work: Finding and fixing performance bottlenecks and organizing data at scale
Favorite thing: We take pride in our output and support each other
School: Programming a Pac-Man in Cal's Intro to AI class
Work: Creating Box's first A/B testing framework still used today
Favorite thing: Learning something from one of my coworkers every single day!
School: Dashboard to visualize and interact with contractor data from the Department of Defense
Work: Custom automated rules in AdWords that optimize CPC bidding
Favorite thing: Scrappy, efficient, and ambitious in our projects
School: The difficulty and subsequent satisfaction of learning R in Statistics II at Fordham
Work: Helping to create a global price book while working in the Images department at Associated Press
Favorite thing: Everyone's selfless pursuit of building an amazing customer-centric business
School: Understanding environmental issues (and proposing solutions) through data and science
Work: Evaluating pipeline data to optimize our recruiting funnel - never ending project!
Favorite thing: Our incredibly authentic team
Work: Buyer persona definition
Favorite thing: Everyone watches out for each other and has everyone's best interest at heart!
School: The intersection of ethical theory and technological innovation at UCBerkeley
Work: Modeling executive equity ownership and stock options at J.P. Morgan
Favorite thing: As an ex college athlete, the teamwork and collaborative focus
School: Probabilistic record linkage algorithms and rule-based matching
Work: Building internal tools to to match, extract and transform data at scale
Favorite thing: The freedom to get wildly creative in exploring new ways to solve problems
School: Dark matter, pulsars, and magnetars in physics seminar on the cosmos at Stanford
Work: Built a model to optimize commuter shuttle routes
Favorite thing: The people
School: Knot theory and its applications in graduate school
Work: Teaching sabermetrics to high school students
Favorite thing: We challenge and support each other every day
School: Studying data in journalism (and sabermetrics) while pitching college ball
Work: Building a dynamic pricing model at DataFox
Favorite thing: Camraderie between engineering and sales
School: International Politics: Methods of Analysis
Work: Gathering customer feedback to influence product roadmap
Favorite thing: We're small, scrappy and we have each others backs!
School: Communication studies where I learned how to communicate based on my audience
Work: The role types identification project where we took a deep dive on what various roles care about.
Favorite thing: The saturation of intelligence and willingness to help
School: Apply statistical regression models to predict fantasy basketball performance
Work: Calculating potential customer fit with standard deviation analysis
Favorite thing: A team that imposes high standards of work quality to create energy rather than consume it
School: calculating phase delay for course on Sound Engineer
Work: Collaborating with Data Scientist at Triax to establish construction worker 'risk probability' score
Favorite thing: Natural collaboration to make everyone better then they were yesterday
School: Data extraction using SQL in Data Analytics for Business
Work: Created scripts to turn CSV fields into custom search terms
Favorite thing: How our company values get actualized
School: Disaggregating signals with Hidden Markov Models
Work: Training our company news classifier to extract signals
Favorite thing: The team's empathy and willingness to teach each other
School: Empirical Analysis and Quantitative Methods at UC Berkeley
Work: Built a data-driven conference strategy based on target account density at DataFox
Favorite thing: Our ability to aggressively pursue seemingly impossible deadlines as a cohesive unit
School: Aeronautical Engineering
Work: Growth Segment Analysis
Favorite thing: The Team Itself
School: I loved art class as I love visualization and cryptography class because it was challenging
Work: Coding our Insights tab, showing users visuals stats for their searches/data!
Favorite thing: kind, understanding, supportive teammates
School: Building long term valuations for investments, mergers, & acquisitions
Work: Running endless manual data cleanse projects for a financial services firm
Favorite thing: The general hunger to create something new; everyone is a builder
School: Studied painting, minored in art history, focusing on Venetian renaissance and African art
Work: Designed a crop insurance platform for farmers who leverage IoT data
Favorite thing: A great team of smart and nice people
School: Probability distributions in statistics
Work: Mapping ADR contribution to revenue + associated activity using SFDC reporting
Favorite thing: The willingness to always help out and motivate each other
School: Nonlinear speech and signal processing for speech pattern analysis at Marquette
Work: Seamless integration of Salesforce in the DataFox application
Favorite thing: Collaboration and camaraderie amongst a great team of smart engineers
School: Business statistics helped put the power of stats into perspective
Work: Working with engineering to give feedback on account scoring
Favorite thing: The constant competitive energy on the sales floor
School: Language dynamics
Work: Optimizing keywords for search
Favorite thing: Incredible collaboration!
School: Market validation. Entrepreneurship 101:Boots on the ground. Boulder, Co
Work: Rethinking sales processes to cut down on time to close
Favorite thing: Hustle. You're never the first one in at 7am or the last one to leave at 8pm
Work: Statistics and infographic designs
Favorite thing: We ship. Quickly.
School: Marketing research on the return rate and customer satisfaction in the cosmetic field
Work: Using big data analytics to grow Marketing ROI
Favorite thing: we are constantly being challenged & helping each other accomplish our goals.
School: Linear programming in management science at WashU
Work: Building pivot table UI for analyzing aircraft data
Favorite thing: Impressive team members
School: Probability theory and linear regressions in statistics
Work: Led cross-object schema mapping and data migration to Salesforce, Pardot, and Outreach.
Favorite thing: Watching how the team lives up to our reputation
School: Conditional probability and data distributions in Statistics
Work: Equity holding optimization for iShares portfolios
Favorite thing: Every single person is willing and able to help each other out
School: Macroeconomics - It's interesting to understand how the world works
Work: Commodities & FX Desk at Bloomberg
Favorite thing: Leveraging data to scale and prioritize projects
...(and we're hiring!)
General Partner, Susa Ventures
SVP, FactSet Research Systems
General Partner, Social Leverage
SVP, Oracle Data Cloud
Former Chairman & CEO, D&B
Founding Board Member, Google
Head of Risk Svcs, S&P Global