AI & Data Student

BrandShield is a brand-oriented digital risk protection company that specializes in monitoring, detection, and removal of digital threats such as phishing attempts, executive impersonation, counterfeit products, trademark infringements, and more.

We are seeking a highly analytical and motivated "AI & Data Student" to join our AI team and contribute to our data generation, prompt engineering, and validation processes. This role is essential for identifying and mitigating customer-facing cybersecurity risks while collaborating closely with our R&D departments to drive product innovation.

Note: This position may not include coding or development tasks.

Responsibilities

• Data Analysis: Analyze large-scale datasets to identify efficiencies, extract key insights, and spot potential technical problems.
• Generation & Prompt Engineering: Design and optimize prompts within our modules to improve data extraction and the overall quality of LLM results.
• Validation: Perform manual and automated testing of datasets and features during both the research and production phases.
• Cybersecurity Risk Management: Utilize R&D tools to identify, measure, and tackle external cybersecurity risks that impact our customers.
• Cross-Team Collaboration: Work alongside various R&D teams to support ongoing research, feature development, and rigorous testing.

What We Are Looking For

• Academic Path: A BA student currently in their second year (with at least 1.5 years remaining).
• AI Experience: Hands-on experience working with various Large Language Models (LLMs).
• Research Mindset: Proven ability to conduct research, form hypotheses, run experiments, and provide data-driven suggestions.
• Analytical Nature: You are genuinely enthusiastic about numbers and data.
• Personal Attributes: You are highly organized, detail-oriented, rapid learner, and a consistent team player who brings a positive attitude to the workplace.

Advantages

• Prior experience in Prompt Engineering.
• Experience with "vibe coding" workflows and a solid understanding of LLM token economies and optimization.
• Successful completion of an academic-level Statistics course.