MSBA Career roles & paths

The MSBA prepares you for a wide range of careers across data and analytics. This guide introduces some of the roles our graduates pursue, with a brief overview of each and the skills that set strong candidates apart. Use it to explore your options, find the path that fits you, and focus your search.

TRACK 1: BUSINESS & STRATEGY

Closest to the business and the least technical entry point.

Role Name	Description	Key Skills	Example Titles
Functional Business Analyst	A traditional analyst who supports a specific business function or team (marketing, finance, logistics, HR, sales, or product). Improves processes, business metrics, and analyzes data to help decision-makers. Strong domain knowledge matters more here than advanced technical skills.	Excel PowerPoint Business and Domain Knowledge Communication Some SQL and Tableau or Power BI as a plus	Marketing Analyst, Logistics Analyst, Financial Analyst, or Sales & Ops Analyst.
Business Analyst	The bridge between business users and the development teams building new tools or releases. Gathers requirements, spots process improvements, supports QA, testing, and documentation. Needs a balance of business sense and technical fluency, and often reports to a product or project manager.	SQL Excel Data Visualization (Tableau) Requirements Gathering Flow Diagrams Agile and Jira Presentation Skills Basic Data Modeling	Business Analyst, IT Analyst, or Application Analyst.

TRACK 2: ANALYTICS, DATA & AI

From dashboards to the most technical modeling and engineering work.

Role Name	Description	Key Skills	Example Titles
Data Analyst	Turns business problems into data-driven analyses, monitors data and KPIs, builds and maintains reporting and dashboards, and presents insights to business audiences. Lighter on complex modeling than a data scientist, so a well-rounded, business-aware profile matters.	SQL Python or R Excel Tableau and Data Visualization Analytics Business Insight	Data Analyst, Product Analyst, Analytics Engineer, or Business Intelligence Engineer.
Data Scientist	Applies advanced data mining, modeling, and machine learning to generate new insights, from predictive modeling and optimization to NLP. Selects and validates models, understands their limits, and works with business teams to find high-value problems.	Statistics Machine Learning Python Data Mining and Modeling NLP Cloud (AWS, Azure, GCP, BigQuery)	Data Scientist, Senior/Lead Data Scientist, Data Modeler, Statistical Analyst, or Decision Scientist.
Data Engineer	Designs, builds, and optimizes systems for collecting, storing, and accessing data at scale, creating the pipelines that data scientists, ML engineers, and BI teams depend on. A heavily technical, high-demand role with strong programming and cloud skills at its core.	Python SQL Java or Scala Cloud and Big Data (AWS, Azure, Spark, Snowflake, Databricks) ETL Data Modeling Automation	Junior/Senior Data Engineer or Data Quality Engineer.
AI/ML Business Analyst	A business-facing role focused on putting AI to work inside existing workflows. Identifies where AI helps, builds and tests internal copilots and assistants, designs and evaluates prompts, and uses RAG to connect models to company data. Business judgment matters as much as technical skill.	Python SQL Java or Scala Cloud and Big Data (AWS, Azure, Spark, Snowflake, Databricks) ETL Data Modeling Automation	AI Analyst, AI Solutions Analyst, AI Business Analyst, AI Product Analyst, or AI Automation Analyst.
AI/ML Engineer	A technical, engineering-heavy role focused on deploying and serving ML models in production, building APIs for model access, monitoring and retraining models, and maintaining production data and inference pipelines. A growth path from AI/ML Business Analyst, usually reached after one to three years via analyst, data engineer, or data scientist roles.	Python Software Engineering Fundamentals Git Cloud (AWS, Azure, GCP) Docker and Kubernetes MLOps (MLflow, Kubeflow) Model Deployment CI/CD	Machine Learning Engineer, AI Engineer, MLOps Engineer, or ML Platform Engineer.

TRACK 3: CONSULTING, PRODUCT & PROJECT MANAGEMENT

Business ownership and client-facing work.

Role Name	Description	Key Skills	Example Titles
Analytics Consultant/Consultant	Helps clients solve business problems with data tools and solutions, ranging from strategic (closer to data science) to tactical (tool implementation). Acts as an agent of change, guiding clients through new technology and processes. Communication and people skills are essential alongside solid analytical ability and quickly learned domain knowledge, and the work is often deadline-driven.	Communication and Stakeholder Skills Data Analysis and Modeling Python or R Excel SQL Tableau Cloud (AWS, Azure, BigQuery) Business Domain Knowledge	Consultant/Associate Consultant, Data Analytics Consultant, Data Science Consultant, AI or Gen AI Consultant, or Digital Transformation Consultant.
Project/Product Manager	Plans, organizes, and prioritizes work to deliver a project or product, coordinating schedule, budget, staffing, and stakeholders while serving as the liaison between business and technical teams. Product managers set the vision and roadmap, and project managers lead the projects that deliver it, so product roles tend to be more senior with end-to-end ownership.	SQL Excel Data Visualization (Tableau) Requirements Gathering Flow Diagrams Agile and Jira Presentation Skills Basic Data Modeling	Junior/Senior Project Manager, Product Owner, Product Manager, Technical Product Manager, AI Product Manager, or IT Project Manager.