Projects

Domain:  Commercial real estate brokerages.

Problem:  Client team members in these brokerages need to share and organize files within a web application that helps them schedule and structure a real estate deal's progression in a consistent way. To do so, they submitted files to a back-office data team, which verified data integrity. However, they were unable to see the status of submitted file processing or consistent reasons behind various file submission rejections.

Solution:  Led a team of three software developers to construct an uploaded file processing status view in the web application's email inbox for the real estate deal teams, which linked up in real time to matching back-office file processing capabilities enhanced by the same team of developers. See the photo carousel on the left for a visual demonstration of both the client-facing and back-office facing portions of this project.

Domain:  Sales campaigns using e-mail and LinkedIn messages.

Problem:  Clients could manually rate a prospect's overall "sentiment" based on their responses to a given series of pre-programmed emails or messages, but this was somewhat time consuming and potentially tedious.

Solution:  Two separate solutions for automated sentiment analysis of customer text responses were explored. The first approach used a pre-existing commercial API for sentiment analysis, which could be fed a given text and returned a sentiment. When this fell through due to a lack of communication from the commercial API provider, a second approach was explored. This latter approach used an open source latent semantic analysis (LSA) natural language processing technique, which was fed thousands of previous real customer responses and their manually assigned sentiments to train the program for automated sentiment assignment of future customer messages.

Domain:  Sales campaigns using e-mail and LinkedIn messages.

Problem:  The software made use of huge interactor objects, that is, one or more sequential actions modeled in code (e.g., generate an email, send it out) that could have dozens or hundreds of inputs. The quantity of this data made it infeasible to log in a storable format, in its raw form. This meant that the engineering team could not search production logs for errors.

Solution:  A custom logging interface to provide an easy to use logging API for developers. It allowed for data inputs of interest (e.g., a user's ID) to be filtered and logged alongside all accompanying interactor events involved with the data. The end result was searchable error logs for what were previously opaque bugs found buried in a chain of past events.

Domain:  Hiring software for small and medium businesses.

Problem:  The software was being used internally by employees at a recruiting company for creating and editing job posts. Many of these posts are similar in structure and content, especially for positions at larger companies and repeat clients. Greater consistency and lesser tedium was desired in the repetitive creation of job posts.

Solution: A database of shared, curated, editable "snippets" of parts of job ads was developed, along with an associated user interface. This sharable "lego kit" approach allowed for standardized, rapid write ups for job postings.

Domain:  Hiring software for small and medium businesses.

Problem:  The software integrated with numerous application feeds of different hiring sites such as Indeed, LinkedIn, Monster, etc. Facebook was rolling out its own job site, and an integration was desired.

Solution:  Integration with the then nascent Facebook Jobs API, requiring liaising with the implementation team at Facebook in order to push new job postings from our hiring software to their site, and the corresponding webhooks to receive job applications from them.

Domain:  Hiring software for small and medium businesses.

Problem:  The software featured the ability for users to create and edit their own job postings, prior to having the software place them onto numerous job portals. One add-on portion of the job posting feature was a psychometric section, which allowed job posting creators to choose from sets of personality screening questions, or to even add their own. However, this feature was quite outdated and clunky at the time, requiring an update.

Solution:  Substantial enhancement to the platform’s job application workflow.