Python Developer in Providence, RI, United States

Simple website to publish the "USENET Cookbook", an early version of crowdsourcing via a moderated newsgroup. Involved translating TROFF (typeset) files into MongoDB documents by way of JSON, and publishing them through a template-driven Python back-end.

Using map-reduce on AWS to pull from a large meteorological data set and produce maps in R showing wind speed variability.

A method and related system for determining the weighted average conversion (or click-through) probability (WACP) associated with an activity, such as an internet advertising bid activity. The method involves gathering information associated with prior actions relevant to future actions, such as bidding on internet advertisement placement, for example. The gathered information, such as clicks, impressions and conversions resulting from an advertisement placement, is used in an algorithm to calculate the WACP. The method may be carried out as part of a system including one or more computing devices and the execution of instructions embodied in computer-readable media. The algorithm may include the option of weighting the data used, such as by valuing recent data more than older data.

A method is provided for identifying and crediting interactions leading to a conversion, comprising acts of for each of at least one defined time interval, defining a recency factor used to scale a credit amount given to an influencing event occurring during the defined time interval; identifying at least one influencing event that influenced a conversion event; for each of the at least one influencing events, identifying a defined time interval in which the influencing event occurred and accessing the recency factor for that defined time interval; and apportioning the credit amount given to the conversion event among the at least one influencing event according to the recency factor for each influencing event.

Embodiments of the present invention are directed to a search engine system, method, and apparatus comprising a search engine, a participant management system, at least one storage database, a listing database, and at least one feedback mechanism. The search engine accepts input data and utilizes the input data to search for information related to the search terms. Upon retrieval of relevant information, the data analysis module organizes the data related to the search terms into a search results list. To determine the placement of listings upon a search request, the data analysis module determines the probability, for each relevant listing, that the user will ultimately complete the desired action if the listing is shown, wherein the probability of action is determined by a plurality of factors. This probability is then combined with the specific CPA for the advertiser. Transaction information derived from a search and click is returned through the feedback mechanism to the search engine for revised calculation of listing placement.