(Remote) Data Scientist

• Proven backend development skills in Python with experience building APIs, data pipelines, or ML infrastructure. Familiarity with tools like FastAPI and Docker is required; cloud platforms preference given to GCP. • Deep curiosity about how LLMs work along with the ability to reverse-engineer AI search behavior for actionable product features. No specific years of experience mentioned but a track record in taking projects from research to production within fast-moving startup environments suggests at least some relevant industry exposure, though not explicitly stated as required education or certification. • Strong problem-solving skills and comfort working with ambiguous, evolving problems are necessary for the role's responsibilities of model lifecycle ownership from experimentation to production deployment in collaboration with engineering teams. No specific years of experience mentioned but implies a need for relevant industry exposure or education background that would provide such experiences (e.g., an MSc/PhD might be inferred, though not explicitly stated). • Excellent communication skills are required to explain complex technical concepts to customers and stakeholders effectively; no specific years of experience mentioned but suggests a need for relevant industry exposure or education background that would provide such experiences (e.g., an MSc/PhD might be inferred, though not explicitly stated). • Experience with the listed AI models: OpenAI, Claude, Perplexity, Gemini, Llama is required; no specific years of experience mentioned but suggests a need for relevant industry exposure or education background that would provide such experiences (e.g., an MSc/PhD might be inferred). • Bonus points are given to contributions to open-source projects and deploying side/hobby projects, as well as presentations at top ML or AI conferences; no specific years of experience mentioned but suggests a need for relevant industry exposure. Having started a company before is also considered favorable (though not explicitly stated as required). • Fluency in TypeScript and knowledge about the listed data science stack languages, libraries, backend technologies, databases, AI models are expected; no specific years of experience mentioned but suggests a need for relevant industry exposure or education background that would provide such experiences.