Applied Data Scientist – AI Engineer

🕒 August 6

🇧🇷 Brazil – Remote

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 27%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Sciensa

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2010

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

Sciensa is an InfraTech company that designs, builds and operates scalable, secure digital infrastructure and AI-driven products for the financial sector. It provides data and generative AI solutions, API integrations (including Databricks), cloud and cybersecurity services, Salesforce platform implementations, and custom development to help banks, digital banks and fintechs accelerate product delivery, governance and customer journeys. Sciensa also offers talent and consulting services and industry-specific accelerators for finance.

📋 Description

• Develop predictive models and classification algorithms • Build evaluation metrics and model cards • Design experiments to validate models • Ensure explainability and traceability of decisions • Work with solutions based on LLMs

🎯 Requirements

• Experience with Machine Learning in production • Proficiency in Python • Experience with model evaluation and performance metrics • Knowledge of Machine Learning pipelines • Knowledge of LLMs and applied AI • Good communication skills • Systems thinking • Collaborative technical leadership • Ability to mentor • Pragmatic mindset • Ability to work across multiple teams and diverse technology contexts • Experience with RAG, MLOps, Model Governance, and model evaluation frameworks is a plus

🏖️ Benefits

• Opportunity for technical growth • Culture of engineering best practices • Collaborative, results-oriented teams • Environment balancing autonomy and governance • Real and relevant technical challenges • Room to influence architecture and technical decisions • Close collaboration with the business, with direct impact on deliveries • Equipment provided by Sciensa, according to area/project needs