Data Scientist

Wonderful enables enterprises to build, test, deploy, and monitor AI agents for their most complex needs, serving customers and employees seamlessly across voice, chat, email, and back-office systems in any language or cultural context.

Role Overview

• Lead research efforts in machine learning for agentic flows, with a focus on speech/audio understanding and decision-making.
• Explore, design, and evaluate state-of-the-art methods, running rigorous benchmarks and experiments.
• Translate research outcomes into robust, production-ready solutions in collaboration with engineering and product teams.
• Define metrics, measurements, and error analysis frameworks to ensure robustness and reliability in production.
• Monitor deployed models, analyze performance regressions, and drive continuous improvements in accuracy and efficiency.
• Act as the team’s primary research authority, staying up to date with the latest advances in ML, deep learning, and speech/audio processing, and applying them to our platform.
  
  

• 5–7+ years of hands-on experience in data science and applied machine learning.
• Strong background in both classical ML techniques and modern deep learning methods.
• Proven experience deploying, monitoring, and iterating on models in production.
• Experience designing and executing rigorous experiments, benchmarks, and error analyses.
• Familiarity with agentic or sequential-flow systems (multi-step decision making, fallback logic, orchestration).
• Proficiency in Python and ML/DL frameworks (e.g. PyTorch, TensorFlow, scikit-learn).
• Strong problem-solving skills, curiosity, and ability to learn quickly.
• Great collaborator who values ownership, autonomy, and teamwork.
• Bonus: prior work with speech/audio signals (e.g. ASR, embeddings, signal processing).
• Bonus: research experience (academic publications, applied research projects).