When executed effectively, data science solutions for enterprise applications can achieve a rinse and repeat quality—meaning the process for updating or extending a solution (such as for a new business cycle) becomes streamlined and repeatable. However, reaching this level of maturity is challenging. Unlike traditional software engineering, data science projects face greater uncertainty due to issues like data quality, data availability, and unpredictable model performance. The initial hurdles often involve accurately scoping the problem: understanding the business context, assessing available data, and evaluating constraints such as time, budget, and data uncertainty. Clear communication of the solution, underlying assumptions, and their justifications is also critical and requires both experience and skill. While LLMs can assist with coding and documentation, they cannot replace the nuanced expertise needed for business problem scoping and data comprehension. This is where my experience can add significant value.

I am a seasoned data scientist looking for remote consulting data science projects. Please see this link for a professional summary and published work (google scholar link). The services section of this website provides some representative services that I can provide. These are representative. If you have a business task that you want to augment or implement with data science, I would appreciate a conversation to explore if this task falls in my wheelhouse. I have developed a simple open source tool to document and maintain analytics and machine learning projects . Please check this repository for some sample recipes created with this tool. The quality of open-source data science and online collaboration tools available today make remote work a plausible model for the right match.