Atomic is a venture studio that co-founds companies by pairing founders with the best ideas, teams, and resources, and funding those with the most potential.
The company is a stealth-mode, AI-native startup focused on reimagining how e-commerce brands connect with customers, starting with revenue recovery.
The first product is a white-glove recovery tool that transforms drop-off into conversions, utilizing precision, personalization, and zero-party data.
The Data Science Lead will be responsible for spearheading the design, experimentation, and optimization of the LLM-driven platform.
This role involves partnering with the CTO and core team to define and scale the approach to data, experimentation, and model-driven development.
The Data Science Lead will own the experimentation strategy for LLM-powered agents and lead the development of performance evaluation frameworks.
Requirements:
The candidate must be a hands-on data science leader who enjoys solving open-ended problems with rigorous experimentation and strong intuition.
Experience working with LLMs in production and a deep understanding of their quirks, edge cases, and data needs is required.
A strong grasp of building and tuning evaluation systems for agents, prompts, and pipelines is necessary.
The candidate should be comfortable with ambiguity and eager to work in fast-paced startup environments.
Experience defining success metrics and building dashboards or analytics infrastructure that supports iterative learning is essential.
The candidate must be a collaborative partner to engineering and product teams, with a clear point of view and a humble, pragmatic approach to decision-making.
Understanding trade-offs between statistical rigor and startup speed is important, knowing when good-enough is sufficient.
The candidate should be excited to lead data science from day one and build systems, practices, and culture from the ground up.
Benefits:
The position offers a competitive salary along with meaningful early-stage equity.
Title and leveling are flexible based on the candidate's experience.
The company is focused on building a diverse and inclusive workforce and encourages applicants who may not meet 100% of the qualifications to apply.
