Helping our customers build data products!

While I enjoyed my time talking to customers, I've transitioned to a more cross-functional role. I manage our applications, working on products and features for our customers, rolling out changes to the wider team, and helping keep our data pipeline team aligned with revenue needs. I work closely with our director of product to:+ Develop an applications roadmap taking in company goals, customer feedback, and front-line feedback from our revenue team. Balancing bug fixes with new features and cleaning up the technical debt that PDL developed over 5 years.+ Building internal tools for monitoring customer data and adoption. Working with our CS, Sales, and Technical services team to make these tools as useful as possible.+ Working closely with our marketing and web team to scale up our redefined self service products.+ Running weekly product trainings and weekly feedback meetings for different stakeholders across the business. Taking my tribal knowledge to other parts of our engineering org to help them prioritize better.+ Acting as a final point of escalation for our Solutions team (since I've done their work before)

As we scaled, we realized there was a need to work with our customers' engineering teams more closely. Given my proximity to our sales team in San Francisco, I was getting pulled into more and more sales calls as a Solutions Architect until I fully transitioned into this role where I was responsible for:+ Working with customers on evaluating our data, helping design custom tests and success criteria+ Helping customer post-sale implement our API endpoints and ingest our flat file deliveries+ Coordinating feedback from the revenue org. back to product and running weekly product trainings with the revenue team.

As we scaled the team we realized that a lot of our process wouldn't scale. During this period I was responsible for improving our ability to take in data and assess the quality of partners in our "data union" -- an opt-in data sharing co-op of our customers and partners.+ Worked on developing a standardized way to assess the quality of data sources using our data as a "source of truth". + Built a CLI for tagging sources with additional metadata and wrote a package that converted data sources into a standardized format using the metadata from the CLI.+ Helped improve our entity resolution -- we began moving towards using probabilistic linkage and more proprietary and creative linkage methods in order to enhance the quality of our data+ Co-operated with our sales team to identify customers with good data.

During this period I was responsible for improving our entire data standardization process. Taking a lot of the issues I had identified during my first internship, another engineer and I began to improve our product dramatically from its "first phase".+ Built canonical location, company, and school data using internal data sources. This gave me a good understanding of data pipelines for entity resolution on a small scale, which I later applied to our person data in my next role.+ Created parsers for the over 100 data fields in the people data labs person object.+ Evaluated data sources for quality and lift.

+ Helped identify target accounts using analytics and learning about the use cases of existing customers+ Worked to pull samples for customers and provide support for customers using our API+ Worked closely with engineering team to identify and catalogue issues with our data and API

Worked on fixing bugs on an API written using Node.Js that was failing to properly sort through JSON data based on keyword searches; gained exposure to a REST API, Node.Js, and to JSON formatting.Designed and tested various implementations for a customized newsfeed API and presented on the topic to the CEO.Tested various APIs for image tagging and compared these APIs to microtasking software (Amazon Mechanical Turk). Presented costs and benefits of both solutions to the CEO.