Staff Data Scientist

Responsibilities

• Architect, develop and deploy machine learning systems to production. The main use cases are related to chatbots and search.
• Design and execute experiments.
• Analyze a wide variety of data (structured and unstructured, observational and experimental) to improve current models or create new ones
• Apply the scientific method to develop LLM-powered applications, including testing different prompts, exploring and understanding LLM capabilities, evaluating agents and prompts, assessing AI application guardrails, and building PoCs to validate the feasibility of generative AI for product features.
• Participate actively in discussions and decisions regarding the whole Data Science chapter (Guilds, Design Reviews, Demos, etc.)

What we are looking for:

• People that are seeking to learn and deliver real impact through Data Science
• Expert knowledge of machine learning concepts: regression and classification, clustering, neural networks, feature selection, cross-validation, curse of dimensionality, bias-variance tradeoff, model explainability, etc.;
• Good understanding of the engineering challenges to deploy machine learning systems to production;
• Proficiency in Python;
• Some knowledge or experience with Deep Learning
• Experience with technical advice for other data scientists (technical leadership);
• Excellent written and verbal technical communication skills. We're looking for someone who loves Data Science & Machine Learning and is willing to share his/her knowledge with the other members of the Data Science Chapter;
• Good English skills (verbal and written) is mandatory.

You'll stand out if you:

• Have a MSc. (or Undergrad + intense experience) in machine learning, data science, information retrieval, ranking systems, recommender systems, natural language processing or other relevant fields.
• Have experience with applied generative AI: AI agents, prompt engineering, LLMs finetuning (lora, qlora, peft), LLM routing, LLM monitoring, LLM guardrails
• Have experience working at fast-growing startups.