About the role
We are looking for an Applied Scientist to build the models and decision systems behind Kily's recommendations and actions. The work is grounded in messy, real-world commerce data to help build Kily's core decision intelligence layer: systems capable of understanding complex commerce data, determining why performance is changing, deciding what should be done about it, and ultimately taking actions autonomously at scale. You will work at the intersection of learning algorithms, decision-making under uncertainty, and agentic systems.
Responsibilities:
- Conduct deep analysis of commerce data to derive insights and identify gaps and new opportunities.
- Develop scalable and effective machine-learning models and optimization strategies to solve business problems across advertising, pricing, and listings.
- Define and lead science initiatives from problem framing through production deployment in a high-ambiguity environment.
- Identify and build the sequential feedback loops that make decisions improve over time.
- Design evaluation frameworks to measure the quality and business impact at scale.
- Work closely with engineering, analytics, and product teams to take models from experimentation into production
Requirements:
- 4+ years in applied ML/AI, data science.
- A master's or PhD in a quantitative field is a plus.
- Deep proficiency in Python, SQL, statistics, and data analysis.
- Hands-on experience developing, deploying, and maintaining the end-to-end lifecycle of machine learning models.
- Experience with LLMs, fine-tuning, AI agents, optimization, or sequential decision systems is a strong plus.
- Exposure to e-commerce, marketplaces, advertising, or pricing data is valuable but not essential.
- Strong problem-solving and communication skills; ML research experience is a plus.