As the field of artificial intelligence (AI) and machine learning (ML) continues to prioritize fairness and the concern for historically marginalized communities, the importance of intersectionality in AI research has gained significant recognition. However, few studies provide practical guidance on how researchers can effectively incorporate intersectionality into critical praxis. In response, this paper presents a comprehensive framework grounded in critical reflexivity as intersectional praxis. Operationalizing intersectionality within the AI/DS (Artificial Intelligence/Data Science) pipeline, Quantitative Intersectional Data (QUINTA) is introduced as a methodological paradigm that challenges conventional and superficial research habits, particularly in data-centric processes, to identify and mitigate negative impacts such as the inadvertent marginalization caused by these practices. The framework centers researcher reflexivity to call attention to the AI researchers' power in creating and analyzing AI/DS artifacts through data-centric approaches. To illustrate the effectiveness of QUINTA, we provide a reflexive AI/DS researcher demonstration utilizing the \#metoo movement as a case study. Note: This paper was accepted as a poster presentation at Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) Conference in 2023.

With the increasing amount of data and the increasing awareness of data-driven culture, global businesses strive to adopt a data science approach. Undoubtedly, data-driven intelligence has become the highest parameter to succeed in the digital world. However, Covid changed the world overnight. Most data science models became useless--at least for some time. Everyone raced to retrain and redeploy their existing data science models.

"…the branch of artificial intelligence that brings it out of the lab and into the real world…" [1] In essence, applied AI is taking scientific research and applying it to business problems. It would be helpful to start with a definition of machine learning.

Data science and computer science often go hand-in-hand, but what makes them different? What do they have in common? After holding several different roles in data science departments at various companies, I have discovered some general qualities common to the data science process, along with how computer science is incorporated into that process as well. Anyone who currently works in or who is interested in entering either field should note the differences between these two disciplines, as well as when one requires concepts and principles from the other. Usually, a data scientist will benefit from learning computer science first and then specializing in machine learning algorithms.

Business Intelligence (BI) descriptive approach has shifted toward a more predictive and prescriptive analysis. Based on these changes, the analytic framework has been revised to include a data science layer. The combination of traditional business intelligence and data science is seen as the future of the field. Consequently, emerging cloud-based services are now presented as one integrated service. They merge different technologies, including data warehouse, machine learning framework, and visualization tool in order to facilitate access to both data analysts and data scientists.[3]

Whereas data scientists in the past have had to use quite a bit of code to come up with testing, comparing, and evaluating machine learning algorithms, there has recently been an emergence of libraries in Python that reduce that work significantly. One of those libraries is PyCaret [2], by Moez Ali, an open-source library with small amounts of code required that ultimately allows you to quickly prepare data to deploy your final model in minutes. There are several benefits, which are native to the functions of PyCaret. Some of those benefits include ease of use, efficiency, and learning about new machine learning algorithms. In addition to those more board benefits, there are also around four main steps that all PyCaret models follow that serve as easy ways to execute a process that otherwise, could take more time without this library.

Overlap between these two popular tech roles is sure to happen, so let's dive deep into what skills are required for both roles and what makes them different. In general, data scientists can expect to work on the modeling side more, while machine learning engineers tend to focus on the deployment of that same model. Data scientists focus on the ins and outs of the algorithms, while machine learning engineers work to ship the model into a production environment that will interact with its users. I will be describing these top skills by personal experience in 2021. I have seen a lot of articles communicate other skills and tools that data scientists use, but I want to describe the ones that most people I know, including myself, use daily.

I am often asked about the state of data science and where we sit now from a maturity perspective. The answer is pretty interesting, especially now that it's been more than a year since COVID-19 rendered most data science models useless -- at least for a time. COVID forced companies to make a full model jump to match the dramatic shift in daily life. Models had to be rapidly retrained and redeployed to try to make sense of a world that changed overnight. Many organizations ran into a wall, but others were able to create new data science processes that could be put into production much faster and easier than what they had before.

Data is the lifeblood of modern businesses. Increasingly, getting the most out of our organization's data with accurate insight and understanding makes a real difference to business success. As a result, the data scientist has become a critical hire for companies of all sizes, whether the job is a specialized position in IT or embedded in a business unit. Nevertheless, it isn't always clear what we mean by the term data scientist. A highly qualified data analyst?

Data Scientists and Software Engineers can work hand-in-hand, while some work completely apart from one another, so you can expect to see some similarities and differences between them. A usual company team encompasses a Data Scientist, Machine Learning Engineer, Product Manager, and Software Engineer (a blend of Product and Engineering). Oftentimes, one is already a Software Engineer and will transition to become a Data Scientist and vice versa. Below, I will be describing the skills, goals, differences, and similarities of each role and between each role. The goal of this article is to highlight these characteristics to better understand these positions, how they work with one another, and to start a discussion that can help you decide which role you would like to stay in or change to.