Ahmad Shah

Currently working as a senior software engineer at MongoDB, on the performance analysis team, helping to build tooling to collect, measure and report on MongoDB’s performance. Tooling was designed to automatically measure performance of MongoDB’s primary code base and gather/flag signals for code changes that may have caused regressions. My work ensured that MongoDB’s latest releases were the most performant to date. Tooling was built primarily in Python for backend development, including algorithms and APIs. The frontend consists of multiple components: some were developed using Dash (Python-based), while others were built primarily with JavaScript (ReactJS) for greater flexibility and interactivity. Previously worked as a technical lead for Capital One on the Canada card campaign tech team developing software tools for automating current manual processes based on AWS tech stacks (EC2, S3, Lambda etc). Code is being developed up to enterprise tech standards on a platform shared among several teams and is actively maintained in well governed GitHub repositories, which is deployed via Jenkins pipelines. Code is developed using Python, Apache Spark, Scala and SQL with thorough unit testing and documentation in both QA, pre-production and production environments. Also, running country wide campaigns, using advanced SQL techniques on the aforementioned platforms, with direct customer impact (I.e. direct mail, e-mails, terms changes etc). Also, worked on the NetDx project (machine learning techniques). NetDx is a patient classifier algorithm that can integrate several types of patient data into a single model. It does this by converting each type of data into a view of patient similarity; i.e. by converting the data into a graph in which more similar patients are tightly linked, while less similar patients are not. Can be found at: http://netdx.org Also, worked with machine learning as a method to extract unannotated regulatory regions through out the human genome that may potentially carry recurrent driver mutations in cancer cells. My Publications: https://f1000research.com/articles/9-1239 https://www.biorxiv.org/content/10.1101/084418v4.full https://epigeneticsandchromatin.biomedcentral.com/articles/10.1186/1756-8935-7-29 https://pubmed.ncbi.nlm.nih.gov/24319001/