Exploring careers in AI and Machine Learning for University of Cincinnati students

As a student at the University of Cincinnati, you’re perfectly positioned to dive into the transformative fields of artificial intelligence (AI) and machine learning (ML), which are revolutionizing industries like healthcare, finance, and technology. The field offers strong prospects — the U.S. Bureau of Labor Statistics projects 36% job growth for data scientists through 2033, with median earnings over $110,000 per year.  

This article guides UC students on exploring and preparing for AI/ML careers, by leveraging UC’s resources, co-op programs, undergraduate research programs, and Cincinnati’s tech ecosystem. Inspired by UC student Addie Salvador’s co-op experience at the National Renewable Energy Laboratory (NREL), you can see how your peers are shaping the future of AI.

AI and ML are transforming various sectors, from energy to healthcare, creating a surge in demand for skilled professionals. According to McKinsey, the global AI market is projected to reach $1.8 trillion by 2030, fueling demand for roles like machine learning engineer, data scientist, AI research scientist, robotics engineer, computer vision engineer, and AI ethics specialist. Addie Salvador’s story, highlights how a UC mechanical engineering student applied AI to electron microscopy during a co-op at NREL, demonstrating the real-world impact UC students can achieve.

AI/ML offer many roles, each requiring specific skills. UC students can explore:

1. Machine Learning Engineer: Builds and deploys ML models, requiring Python, R, and frameworks like TensorFlow or PyTorch.
2. Data Scientist: Uses ML and statistics to extract insights, leveraging tools like SQL and data visualization.
3. AI Researcher: Advances AI algorithms, often requiring a Ph.D. and expertise in math and computer science.
4. AI Product Manager: Bridges technical and business teams for AI product development, needing strong communication skills.
5. Ethics and Policy Specialist: Addresses AI’s societal impacts, like bias and privacy, blending technical and social science knowledge.

For UC students inspired by Salvador’s achievements, the path to a career in AI/ML is accessible through the university’s academic and co-op programs. Here’s how Bearcats can get started:

• Relevant Majors: Pursue degrees in UC’s College of Engineering and Applied Science, such as Computer Science, Electrical Engineering, Industrial and Systems Engineering, or Mechanical Engineering (like Salvador’s). The School of Information Technology also offers programs in Data Analytics and Artificial Intelligence.  Additionally, a degree in Mathematics, which provides a strong foundation in analytical skills essential for AI and ML, can complement technical expertise in these fields.
• Minors and Certificates: Consider a minor in Computer Science or a certificate in Data Science to build specialized skills. For those who have completed an undergraduate degree, Lindner College of Business offers a graduate certificate for Artificial Intelligence in Business.
• ACCEND Program: Many undergraduate engineering students participate in ACCEND,UC's accelerated engineering program, which allows them to add a master’s degree, often choosing the 1-year, industry-focused Master of Engineering (MEng). UC offers MEng programs in Robotics/Autonomous Systems and Artificial Intelligence, providing a comprehensive education that aligns with industry demands.

• UC’s co-op program, a hallmark of the university, is a game-changer. Salvador’s NREL placement exemplifies how co-ops offer real-world exposure. Students can seek co-op opportunities with tech firms, research labs, or companies like Siemens or GE, which have AI-focused roles.
• Work with UC’s College of Cooperative Education and Professional Studies to target co-ops involving AI/ML projects. Salvador’s role at NREL, integrating AI with microscopy, shows how co-ops can bridge academic theory and industry application.

• Programming: Master Python, R, or C++ through UC’s summer bootcamps, Python workshops, Coursera, or UC’s LinkedIn Learning.
• Tools and Frameworks: Self-study ML frameworks like PyTorch or scikit-learn using UC’s access to LinkedIn Learning or free resources like Coursera.

• Join UC research labs, such as the Intelligent Robotics and Autonomous Systems Laboratory or the Center for Global Design and Manufacturing, which explore AI applications. Faculty-led projects can mirror Salvador’s work in applied ML.
• Participate in hackathons, like MakeUC, or national/global events like HackMIT or HackZurich to solve AI challenges. Watch for competitions hosted by UC’s 1819 Innovation Hub to build ML models and network with peers.

• Attend career fairs and networking events: Utilize Handshake (login with your UC username and password) to connect with AI-driven companies. Salvador’s NREL role underscores the value of research institutions as career launchpads.
• Join student groups: Engage with UC’s Association for Computing Machinery (ACM), UC IEEE or UC Artificial Intelligence Society to collaborate on AI/ML projects and learn from industry speakers.

For Bearcats, UC’s co-op program, academics, and vibrant tech community provide a launchpad into AI and ML careers. Whether automating microscopes like Salvador or revolutionizing industries, UC students can follow her lead, blending technical abilities and curiosity to shape an AI-powered future.

Featured top image of Addie Salvador at work inside the National Renewable Energy Laboratory (NREL) in Golden, Colorado. Photo/Brooke Buchan, NREL.