דרושים Data Operations Analyst בתל אביב

Dig is seeking a Data Operations Analyst to ensure the quality and performance of our product's core intelligence! This role is central to our data operations, focusing on the complete lifecycle of prompt templates - from their initial design and testing to ongoing monitoring and improvement.

You will collaborate closely with our Customer Success, Product, and Algorithm teams to ensure our data processes are accurate, efficient, and consistently deliver high-quality results.

Key Responsibilities

• Manage and organize the central Prompt Library, ensuring all templates are up-to-date, documented, and optimized.
• Monitor and report on prompt performance by tracking key metrics via dashboards, and implement alerts to flag performance drops.
• Conduct error analysis on failed data samples to identify dominant failure patterns.
• Lead the optimization of underperforming templates through prompt engineering on golden datasets.
• Manage the deployment of improvements to production and maintain a clear change log.
• Work cross-functionally with Product to set acceptance criteria and with Ops/CS to identify and address real-world template failures.

Requirements

• Bachelor’s degree in Data Science, Statistics, Industrial Engineering, Information Systems, or a related quantitative field.
• 0–2 years in AI model evaluation, data operations, or a similar analytical role.
• Demonstrated ability in prompt engineering for LLMs, including designing, A/B testing, and optimizing prompts to enhance performance (can be shown through academic, personal, or professional projects).
• Strong data analysis skills and familiarity with tools for building dashboards, validating data, and analyzing performance.
• Proficiency in SQL, Python, or a similar scripting language for data manipulation is an advantage.
• A self-starter who thrives in a startup environment, taking ownership of challenges and driving solutions with minimal tooling.
• A proactive and curious mindset with a passion for solving complex data puzzles.