Brandeis Online

May 18, 2026

Discover how program chair Dr. Keishalee Shaw leads Brandeis Online's Applied Data Science and Decision Analytics program — uniting technology, ethics, and innovation to prepare students for data-driven leadership in today's digital AI economy.

What is your current job title? How would you describe your role?

My work sits at the intersection of a few different worlds, and I think that's what makes it so energizing. I'm the Founder and Principal of STRATA Responsible AI, where I advise organizations on what it actually takes to move from broad AI principles to practical governance, the frameworks, the operational controls, the audit-ready processes that let organizations adopt AI confidently and responsibly. Before founding STRATA, I spent over a decade at Blue Cross Blue Shield of Massachusetts working on enterprise AI governance efforts, supporting generative AI risk assessments, and partnering across engineering, legal, compliance, and product teams. That experience gave me a frontline view of where AI governance succeeds, and where it quietly fails.

At Brandeis University, I'm an adjunct professor and program chair for the Master of Science in Applied Data Science and Decision Analytics. In that role, I'm not just teaching; I'm helping shape the program, what students learn, and how those learning experiences connect to real professional challenges. I hold a Ph.D. in Strategic Leadership, graduate degrees in healthcare management and analytics, and certifications in AI product design and responsible innovation. Across all of these roles, the throughline is the same: helping people and organizations harness data and AI in ways that are not just technically powerful, but ethically sound and strategically grounded.

Why did you decide to get involved in the leadership of the Applied Data Science and Decision Analytics program at Brandeis Online?

Honestly? Because I kept seeing the same gap, and it frustrated me. Organizations were trying to scale AI, but they didn't have enough professionals who could sit at the intersection of technical skill, strategic thinking, and ethical judgment. They had great data engineers who didn't know how to communicate findings to leadership. They had smart analysts who had never thought seriously about bias or governance. And they had executives who wanted to be data-driven but lacked the translators who could bridge the technical and the strategic.

I've spent my career working in that gap, and I believe deeply that education is one of the most powerful levers for closing it. When the opportunity came to help design a program at Brandeis that could build exactly those kinds of professionals, people who can engineer a system and audit it for bias, who can build a dashboard and tell a compelling story with it, who can deploy a machine learning model and govern it responsibly, I didn't hesitate. Brandeis has a longstanding commitment to social justice, ethical inquiry, and academic rigor. That alignment with my own values made the decision easy.

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What makes Brandeis Online’s MS in Applied Data Science and Decision Analytics unique? What sets it apart from similar programs at other schools?

Most data science programs are excellent at developing either technical depth or analytical breadth, but very few do both while weaving in responsible AI and governance as genuine requirements rather than optional electives.

That's where we're different. Brandeis foregrounds ethical, sustainable, and applied AI within an integrated, workforce-aligned degree. Responsible AI isn't an add-on; it runs through every course like a spine.

We also take sustainability seriously in a way that's genuinely rare. Students don't just learn to build data pipelines; they learn to evaluate those systems for energy efficiency and carbon impact. They learn that the environmental cost of large model training is a governance question, not just an engineering one. And they leave knowing how to design cloud architectures with built-in sustainability metrics. That's forward-looking in a way that reflects where industry and regulation are heading.

Finally, the program is unapologetically applied. Every assessment mirrors real workforce deliverables: executive memos, governance audits, model deployment reports, and interactive dashboards. Our students graduate with a portfolio, not just a transcript.

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What courses in the Applied Data Science and Decision Analytics program are important to highlight and why?

I could honestly talk about every course in the curriculum, but let me highlight a few that I think capture what makes this program distinctive.

Responsible and Sustainable AI, Ethics, and Governance is a cornerstone. Students learn to conduct audits of AI systems for bias, fairness, and sustainability. They work through real governance frameworks, the EU AI Act, the NIST AI Risk Management Framework, and IEEE's Ethically Aligned Design principles. They examine case studies of governance failures in healthcare, criminal justice, hiring, and public infrastructure, and they design carbon-aware model development workflows. This isn't a theory course; it's where students develop the practical judgment that separates a good data scientist from a responsible one.

Generative AI and Large Language Models in Analytics is the elective I'm most excited about for students who want to be at the cutting edge. We explore prompt engineering, retrieval-augmented generation (RAG), hallucination mitigation, fine-tuning strategies, and the real governance challenges of deploying generative AI in enterprise settings, data privacy risks, language model bias, and regulatory considerations. Given that Gartner recently estimated 50% of GenAI deployments are at risk of failure or abandonment due to architectural and governance gaps, students who leave this course with both technical fluency and governance instincts will be genuinely rare.

And then there's the Capstone: Applied Analytics Project, the culminating experience where student teams partner with a real or simulated sponsor organization to deliver a complete, production-ready analytics solution. It encompasses data engineering, statistical or ML analysis, responsible AI practices, visualization, and executive communication. It's where everything comes together, and it's the primary portfolio artifact students take into the job market.

What are you most looking forward to about your new role at Brandeis Online?

The community. I know that might sound like a cliché answer, but I mean it in a very specific way. This program draws people from remarkably different backgrounds: mid-career professionals in healthcare or finance who want to level up their analytics capabilities, cloud engineers who want to add depth in governance, policy researchers who want to extend their analytical impact, and compliance managers who want to lead AI ethics initiatives. When you put those people in a room together, even in a virtual asynchronous room, something remarkable happens. They challenge each other's assumptions. They bring domain knowledge that enriches the coursework in ways that no textbook could.

I'm also genuinely excited about what it means for students to build skills right now. We're at an inflection point in AI adoption, and the professionals who graduate from this program over the next two to three years will step into roles that genuinely need their expertise. That's a rare feeling in education, the sense that you're preparing people for something that is already here, urgently needed, and growing. 

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The world doesn't need more data; it needs more people who know how to use it wisely. If you're ready to build the technical skills, the ethical judgment, and the strategic fluency to lead in an AI-powered world, I'd love to have you in this program.

Learn more about the Master of Science in Applied Data Science and Decision Analytics at Brandeis Online, and take the first step toward becoming the kind of data professional that organizations everywhere are searching for.