Kily is an AI company bringing autonomy to digital commerce growth. We build autonomous agents that manage Advertising, Pricing and Listings for brands and sellers across commerce marketplaces.

Performance in modern commerce shifts constantly - across marketplaces, categories and cities - faster than teams can manually track, diagnose and act on. Kily's agents work continuously against real business objectives with each brands unique context, objectives and operating constraints and keeping humans in the loop where it matters.

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 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.

What You'll Do :

- Conduct deep analysis of commerce data to derive insights, and identify gaps and new opportunities

- Develop scalable and effective machine-learning models and optimisation 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

What We're Looking For :

- 4+ years in Applied ML/AI, Data Science. Masters 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, optimisation or sequential decision systems is a strong plus

- Exposure to ecommerce, marketplaces, advertising or pricing data is valuable but not essential

- Strong problem-solving and communication skills; ML research experience is a plus