Applied AI Data Scientist (Brazil)

Role overview Work as an applied data scientist on a combination of client engagements, internal product work, and exploratory research. You will use machine learning and statistical reasoning to solve practical problems, help create meaningful products, and investigate advanced AI topics while working with substantial independence.

Responsibilities - Design and deliver data-driven solutions for real-world product and client challenges. - Build and evaluate systems using large language models, including prompt strategies, retrieval, vector search, multimodality, fine-tuning, and model evaluation. - Use experimental design, causal reasoning, uncertainty quantification, and rigorous validation to guide technical decisions. - Develop or train natural-language-processing and computer-vision models with modern deep-learning frameworks. - Work closely with designers, engineers, and other collaborators to turn research and analysis into usable outcomes. - Identify and pursue promising research or product concepts that could become larger internal initiatives.

Requirements - Fluency in Python and a record of delivering data-science work with measurable real-world impact. - Experience across the LLM lifecycle, including prompting, retrieval-augmented generation, few-shot techniques, embeddings or vector databases, multimodal workflows, fine-tuning, and evaluation. - Strong foundations in statistical and causal machine learning, experimental design, uncertainty, and model assessment. - Deep-learning experience with NLP or computer vision using PyTorch, TensorFlow, or JAX. - Self-management, ownership, adaptability, and comfort working through short iterative delivery cycles. - Clear English communication, product and UX sensitivity, and an effective collaborative style.

Nice to have - A self-managed project taken from initial concept through launch. - Experience in startups or client-facing work, plus enthusiasm for AI alignment and the long-term challenges of advanced AI.

Benefits and work setup The role is described as remote-first, with flexible schedules and office access. The source also mentions performance-based raises, employee equity, research and development opportunities, mentoring, regular knowledge sharing, and an annual team retreat.