Applied AI Data Scientist

Role overview Work as an applied data scientist across client projects, internal product initiatives, and exploratory research. The role combines practical machine learning delivery with experimentation and research into advanced AI systems, requiring both technical depth and the creativity to turn ambiguous problems into useful products.

Responsibilities - Build data-driven solutions that address real-world product and client needs. - Develop, evaluate, and improve systems involving large language models, including retrieval, prompting, multimodality, fine-tuning, and evaluation. - Apply statistical and causal reasoning to experimental design, uncertainty quantification, and rigorous model assessment. - Train or adapt natural-language-processing and computer-vision models for practical applications. - Collaborate with multidisciplinary teammates and communicate technical findings clearly. - Propose and pursue high-impact research ideas that may develop into new internal ventures or products.

Requirements - Strong Python programming ability and professional data-science experience delivering outcomes used by real users or organizations. - Practical understanding of the LLM lifecycle, including prompt engineering, retrieval-augmented generation, few-shot methods, vector databases, multimodal systems, fine-tuning, and evaluation. - Expertise in statistical or causal machine learning, experimental design, uncertainty, and model evaluation across tabular, time-series, or foundation-model tasks. - Experience building or training NLP or computer-vision models with PyTorch, TensorFlow, or JAX. - Ability to work independently, take ownership, and deliver in an iterative Kanban or Scrum environment. - Strong English communication, product and UX awareness, and a collaborative approach.

Nice to have - Experience taking a self-directed project from idea to launch. - Startup experience, client relationship management, or a strong interest in AI alignment and its broader societal implications.

Benefits and work setup Remote-first work with flexible scheduling and the option to work from an office. The source also describes performance-based pay growth, employee equity, access to research and development opportunities, mentoring and knowledge sharing, health insurance, office lunches, and an annual team retreat.