Program Type: Paid Professional Fellowship (Summer Internship)

Program Duration: 12 Weeks (June 1st, 2026 - August 21st, 2026)

Time Commitment: 34 hours per week (~400hours total)

Location: Hybrid. Activities include virtual collaboration, potential in-person industry engagement, and coordination with technical, workforce, and ecosystem stakeholders across the BioHealth Capital Region.

Compensation: Paid internship. $30/hr

Program Overview:

The BioBuzz BioCatalyst Fellowship is a structured, high-impact professional development program designed to provide graduate students and recent graduates with immersive exposure to the life sciences ecosystem.

The AI/Machine Learning Workforce Applications Track is designed for individuals interested in developing and improving applications that use artificial intelligence and machine learning to develop solutions to complex workforce, talent, and industry applications within the life sciences industry. This role focuses on supporting the development of AI-driven tools and frameworks that enhance how talent is identified, developed, and connected to opportunities across the BioHealth Capital Region.

Fellows in this track will contribute to the development, design, and improvement of AI-powered applications, including career assistants, skills analysis platforms, workforce dashboards, skills validation tools, talent-matching systems, and career-pathway mapping solutions. This includes gaining exposure to how data, technology, and workforce strategy intersect to solve complex talent challenges in a rapidly evolving industry.

Through this experience, fellows will develop a foundational understanding of applied artificial intelligence in a real-world, startup-like environment, while contributing to initiatives that support workforce accessibility, career navigation, and ecosystem growth.

This role also directly supports BioBuzz’s mission to strengthen the life sciences workforce pipeline by leveraging technology to connect talent with opportunities better and provide more transparent, data-driven career pathways.

Core Responsibilities:

• Improve job data ingestion and pipeline quality using AI and ML tools
• Mine job posting data to build a structured life sciences role and skills taxonomy — the foundational input for SkillSeq matching
• Develop skill → job mapping logic and scoring prototype (SkillSeq v1)
• Generate weekly skills demand insights to feed newsletter content and ecosystem intelligence
• Recommend and configure the tool stack for the shared Maryland Life Sciences Ecosystem Intelligence Dashboard in Week 1
• Contribute the jobs, roles, and skills data layer to the dashboard

Key Outputs:

• Optimized job data pipeline
• Optimize platform personalization and recommendation engine
• Improve Life sciences role + skills taxonomy (built from real job posting data)
• SkillSeq v1 prototype with % match scoring
• Weekly jobs and skills demand insights
• Dashboard jobs + skills intelligence layer

Fellowship Responsibilities:

Fellows will contribute to BioBuzz initiatives across four primary areas:

• AI / ML Application Development:

Fellows will support the research and development of AI-powered workforce applications designed to enhance career navigation and talent matching within the life sciences ecosystem. This includes contributing to early-stage product concepts such as career assistants, workforce dashboards, skills validation tools, and pathway mapping platforms. Fellows will assist in exploring how machine learning models can be applied to workforce data to generate insights and improve decision-making.

• Data Preparation & Model Experimentation

Fellows will work with structured and unstructured data related to workforce trends, job roles, skills, and career pathways. This includes supporting data cleaning, organization, and preparation efforts, as well as assisting in model experimentation and testing. Fellows will gain exposure to how data pipelines are developed and how models are iteratively refined to improve outputs and usability.

• Workforce Insights & Ecosystem Analysis

Fellows will analyze workforce trends within the life sciences ecosystem, including skills demand, role evolution, and talent gaps. This includes translating data into actionable insights that inform product development and strategic decision-making. Fellows will contribute to understanding how workforce dynamics vary across different sectors, including biopharma, startups, and supporting organizations.

• Documentation & Product Concept Development

Fellows will support documentation of AI workflows, model logic, and product concepts to ensure clarity, scalability, and alignment across stakeholders. This includes contributing to user-focused design thinking by helping define how tools will be used, who they serve, and how they create value. Fellows will also assist in translating technical work into clear, accessible explanations for non-technical audiences.

Minimum Qualifications:

• Recently completed undergraduate degree or currently pursuing a graduate degree (Master’s or equivalent)
• Strong interest in artificial intelligence, machine learning, data science, or technology applications within life sciences
• Demonstrated ability to work with data and analyze complex information
• Strong written and verbal communication skills

Preferred Qualifications:

• Academic or professional background in data science, computer science, engineering, mathematics, or related field
• Familiarity with programming languages such as Python or R and basic machine learning concepts
• Interest in workforce development, talent strategy, or career pathway design
• Ability to translate technical outputs into user-friendly insights or applications
• Strong organizational and time management skills
• Experience working on technical, analytical, or product-oriented projects

Core Competencies:

• Analytical thinking and problem-solving
• Technical curiosity and willingness to learn new tools and frameworks
• Ability to connect technical work with real-world applications
• Strong communication skills across technical and non-technical audiences
• Attention to detail and structured thinking
• Proactive and self-directed work style

Professional Development Opportunities:

Through this fellowship, participants will gain:

• Hands-on experience applying AI and machine learning in a real-world context
• Exposure to workforce technology and data-driven talent strategies
• Experience working in a startup-like, product development environment
• Development of a technical and strategic project portfolio
• Networking opportunities with professionals across life sciences, technology, and workforce development
• Insight into emerging career pathways at the intersection of AI and biotechnology

About BioBuzz

BioBuzz is a leading life sciences media and workforce development platform dedicated to amplifying the companies, innovation, and talent shaping the BioHealth Capital Region and beyond.

Through storytelling, community engagement, and strategic workforce initiatives, BioBuzz connects students, professionals, and organizations while strengthening the regional biotechnology ecosystem.