Grupo QuintoAndar | Staff Data Scientist

QuintoAndar; United States; Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Remote-first

Profit sharing

Health insurance

Childcare subsidy

Home office allowance

Employee assistance program

Job summary

QuintoAndar is seeking a staff-level data scientist embedded in product squads to ship ML-driven features across search, recommendations, pricing, credit, and conversational systems. The role focuses on productionizing models and advancing generative AI capabilities.

You'll blend hands-on modeling with technical leadership, mentor peers, and contribute to the data science community of practice through reviews, demos, and guild activities.

Qualifications

• 3+ years applying data science and deploying ML models in production.
• Strong foundations in regression, classification, clustering, neural nets, feature selection, cross-validation, explainability.
• Understanding the engineering challenges of deploying ML systems.
• Proficiency in Python with some deep learning experience.
• Experience providing technical leadership or mentoring to data scientists.

Responsibilities

• Architect, deploy ML systems to production focusing on chatbots and search.
• Design experiments and analyze data to improve models.
• Develop LLM-powered applications including prompt testing and guardrails.

Skills

ML fundamentals

Python

Production ML

Technical leadership

English communication

Mentoring

Education

Master's degree preferred

Job description

Role overview

A staff-level data science role embedded in product squads that ship machine-learning-driven features to end users. The work spans search, recommendation, pricing, credit, and conversational systems, with a strong emphasis on productionizing models and exploring generative AI. The position is expected to combine hands-on modeling with technical leadership across the broader data science community of practice.

Responsibilities

• Architect, develop, and deploy machine-learning systems to production, with primary use cases in chatbots and search.
• Design and execute experiments and analyze structured, unstructured, observational, and experimental data to improve or create models.
• Apply the scientific method to develop LLM-powered applications, including prompt testing, agent evaluation, guardrail assessment, and proof-of-concept work for generative AI features.
• Contribute to pricing, liquidity, credit, and customer-experience modeling efforts such as automated ticket routing.
• Participate actively in data science chapter activities including guilds, design reviews, and demos.

Requirements

• At least 3 years of experience applying data science and creating machine learning models in production.
• Expert knowledge of machine learning fundamentals such as regression, classification, clustering, neural networks, feature selection, cross-validation, and model explainability.
• Understanding of the engineering challenges involved in deploying machine learning systems.
• Proficiency in Python and some experience with deep learning.
• Demonstrated experience providing technical leadership or mentoring to other data scientists.
• Strong written and verbal technical communication, with mandatory English skills.

Nice to have

• A master's degree (or undergraduate degree plus intense industry experience) in machine learning, data science, information retrieval, recommender systems, NLP, or a related field.
• Hands-on experience with applied generative AI, including AI agents, prompt engineering, LLM fine-tuning, routing, monitoring, and guardrails.
• Background working at fast-growing startups.

Benefits and work setup

• Remote-first model with the option to use an office or partner coworking space up to twice a week.
• Competitive salary and profit sharing.
• Health, dental, and life insurance.
• Childcare and atypical-parenthood subsidies, extended parental leave, and home office allowance.
• Wellhub access and an employee assistance program covering mental health, social, legal, and financial support.
• Days off on birthday, Mother's Day, and Father's Day, plus discounts on education and everyday services.