Dave Schultz Consulting

I help your company get the data you need. Whether it's annotated training data, validation of AI/ML models, or simply the data to run your business.

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Years of experience at the intersection of business, data, and technology

12 years At Amazon, including 3 with Mechanical Turk

20+ years Working in data engineering and data science

4 years Freelance Consultant

4 years Consultant for Deloitte

About Me

I help businesses connect the dots between their ideas, and their data and technology

While my career has spanned roles in software development, business, marketing, and product management, the constant has always been data. Now I help companies define, gather, and validate the data they need to build AI solutions.

My diverse set of experiences is now an asset to my clients, who need someone to help them navigate the software, data, and human challenges of building quality data. Whether you plan to crowdsource your data, work with data vendors, or set up your own team, I'm here to help you navigate the process.

Capabilities

Success in building reliable and repeatable data processes require a range of skills

Product Management

Success starts with specifying clear outcomes and defining how we'll deliver the data we need.

Solution Architecture

Modern serverless architectures are ideal for building robust annotation processes and data pipelines.

Data Science

Combining human annotation with LLMs is unlocking new avenues for data annotation.

Software Development

A complete solution often requires development in multiple languages.

Data Engineering

To be useful, your data needs to be stored efficiently using a structure your users can consume.

UI Design

Quality data relies on creating task interfaces that allow annotators to be efficient and accurate.

Case Study

Data platform development

A large enterprise needed a new solution to manage the process of annotating various types of data. They used data annotations to measure the performance of algorithms and ML models over time as they made changes. Annotations were also used to train new models.

They needed to build a new system that would allow them to distribute the work to a team of in-house annotators located overseas, as well as to vendors and a crowdsourcing service.

We were closely involved in the technical architecture, defined key aspects of the product, and developed the UI design. In addition, we were responsible for building most of the task interfaces annotators used and developed the visual language that was used across all task interfaces.

The resulting system allowed the team to rapidly scale up the number and types of annotations the team could handle.

Case Study

Data gathering

A large enterprise used data gathered from customer websites to better inform their sales team and wanted to expand the data provided to their sales organization.

We developed a multi-stage data pipeline that would leverage crowdsourcing (Amazon MTurk) to have crowd workers review aspects of each website, extract key details, and structure it appropriately for use.

Subsequent work explored various AI/ML approaches to further augment the data pipeline and reduce the reliance on crowd workers. In addition, their existing web scraping process was augmented with a crowdsourcing solution to capture content that would otherwise only be accessible by interacting with JavaScript and CSS on the page. This significantly increased the coverage and data retrieved for customers.