At Character.AI, a Data Scientist is responsible for designing and implementing search retrieval algorithms, optimizing recommendation systems, and supporting LLM post-training optimization. You will establish offline evaluation frameworks to measure system performance, conduct A/B testing to validate enhancements, and collaborate with engineering teams to productionize your algorithms.

To qualify for the Data Scientist role at Character.AI, candidates must possess an advanced degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. Additionally, strong programming skills in SQL and Python, experience with ML frameworks, and expertise in retrieval and ranking algorithms are essential.

Success as a Data Scientist at Character.AI hinges on your ability to conduct high-velocity experiments, choose key insight-generating questions, and measure user engagement metrics effectively. Strong communication skills and cross-functional collaboration are vital, along with technical proficiencies in modern search technologies and recommendation frameworks.

As a Data Scientist at Character.AI, you will work on projects that involve the development of search retrieval algorithms, optimization of personalized recommendation systems, and enhancing LLM functionality through advanced machine learning techniques. Your work aims to continuously improve user experiences across our AI platform.

Absolutely! Experience with Large Language Models (LLMs) is crucial for the Data Scientist position at Character.AI, as supporting post-training optimization and leveraging these models for improved content discovery is a key responsibility of the role.

When optimizing large language models, I focus on techniques such as fine-tuning based on specific datasets, assessing model performance using offline evaluation frameworks, and prioritizing user feedback to inform enhancements. It's critical to iterate quickly and make data-driven decisions.

In my previous roles, I've developed and implemented search retrieval algorithms that prioritize relevance and accuracy. This includes fine-tuning heuristics and metrics to ensure users receive the most pertinent results while continually analyzing performance for further optimizations.

I adopt a systematic approach to A/B testing, including clearly defining success metrics, ensuring randomization to avoid bias, and carefully analyzing the results to validate hypotheses. Continuous monitoring and feedback are key to improving subsequent tests.

In a previous position, I identified inefficiencies in our recommendation system using user engagement metrics. By optimizing the algorithm and implementing new features based on my findings, we saw a significant increase in user interactions and satisfaction.

One challenge I faced was integrating new ML frameworks with legacy systems. I overcame this by advocating for a phased implementation plan, conducting rigorous testing, and providing documentation and support for my team to ease the transition.

To ensure relevance in recommendation systems, I continuously analyze user behavior patterns and segment users based on their interactions. Implementing machine learning algorithms that adapt to changing user preferences is essential for maintaining system effectiveness.

I use comprehensive evaluation frameworks that encompass offline metrics such as accuracy and recall, as well as online metrics like click-through rates and user engagement. Collecting qualitative feedback is equally important for a well-rounded performance assessment.

In previous roles, I've collaborated closely with product, engineering, and UX teams to align our technical capabilities with user needs. Regular sync-up meetings, joint brainstorming sessions, and open lines of communication were key to successful project outcomes.

I approach data discrepancies by first conducting a thorough investigation to identify potential sources of error or misinterpretation. Adjustments are made to data collection methods, and additional validations are implemented to ensure accurate measurement moving forward.

I'm excited about the opportunity to work at Character.AI because of its commitment to innovation in consumer AI and its meaningful impact on users. I'm eager to contribute my expertise in ML and search optimization to help further enhance user experiences across the platform.