Role Description Help support Auto and Home Actuarial team update Base Rate Offset tool into Python. This role requires strong Python skills as well as self-learning skills. - Contributes to the development and implementation of predictive analytics through the application of advanced statistical and analytical techniques in order to deliver data-driven insights supporting business objectives. - Uses appropriate modeling techniques to address business needs. - Utilizes broad knowledge of advanced modeling techniques and procedures to develop new modeling techniques and skills. - Conducts appropriate evaluation of model performance. - Designs and publishes reports to communicate results and track model performance. - Develops various programs including predictor and response variable programs. - Reviews programs to ensure they conform to quality standards. - Supports preparation of internal/external, structured/unstructured data sets to build/rebuild/refresh predictive models. - Creates ad-hoc data analyses, as needed. - Communicates analytics to other modelers as well as to non-technical business partners. - Contributes to the continuous improvement of the modeling process. Qualifications - English Proficiency: Fluent (We work 100% in English) Requirements - Python - Advanced - SQL - Intermediate - Power BI - Intermediate - Excel - Intermediate Benefits - Competitive salary and performance-based bonuses - Comprehensive benefits package - Career development and training opportunities - Flexible work arrangements (remote) - Dynamic and inclusive work culture within a globally renowned group - Private Health and Dental Insurance - Pension Plan - Meals tickets - Life Insurance

Role Description Terray is currently seeking a motivated and creative senior associate data scientist. As an integral member of our Computational and Data Sciences (CDS) team, the candidate will be responsible for analyzing and visualizing data from our computational discovery platforms. - Collaborate with project teams in applying platform tools to identify and prioritize hits for follow-up synthesis, testing, and pipeline progression. - Work closely with computational and assay teams to perform hit validation analyses to evaluate how tArray platform binding profiles translate to off-platform assays. - Partner with screening teams to develop and refine target-specific screening strategies. Qualifications - BS/MS in Cheminformatics, Computational Chemistry, data science, or a related scientific discipline with a strong interest in programming and data-driven research. - Understanding of chemical structures, molecular properties, and compound profiling in the context of high-throughput screening and hit identification. - Working knowledge of Python for scientific data analysis, automation, and workflow support, including experience with libraries such as pandas, numpy, scipy, and matplotlib. - Strong expertise in SQL, including building and querying custom tables for internal workflows. - Strong problem-solving and data analysis skills with the ability to communicate scientific insights effectively. - Experience working with large, real-world scientific or laboratory datasets. - Familiarity with Linux environments and version control tools (e.g., Git). Requirements - Exposure to machine learning methods such as clustering, regression, or pattern recognition is a plus. - Exposure to cheminformatics or scientific software tools (e.g., RDKit, Spotfire, MOE, Schrodinger) is a plus. Benefits - Compensation: $120,000 – $182,000 annually, depending on experience. - Participation in the Company’s stock option plan. - 3% retirement safe harbor contribution. - Fully paid health, dental, and vision insurance for employees, spouses, partners, and families. - Above-market life insurance and disability coverage. - Additional benefits to explore during the offer process.

Role Description Projeto de inovação da ICT ITAÚ em parceria Inova Talentos. Qualifications - Perfil do bolsista: mestrado - Formação: mestrado cursando ou concluído. - Cursos: Ciência da Computação, Engenharia de Computação/Software, Sistemas de Informação, Estatística, Matemática Aplicada, Engenharia Elétrica, Ciência de Dados ou áreas correlatas das Ciências Exatas e Engenharias. Requirements - Pré-processamento e enriquecimento de textos: - Limpeza, tokenização, lematização e remoção de ruídos em documentos textuais. - Extração de características (features) relevantes para modelos de NLP. - Geração e gestão de embeddings: - Criar embeddings de documentos e consultas usando modelos como Sentence‑BERT, OpenAI ada ou similares. - Armazenar e indexar embeddings em bancos vetoriais (FAISS, ChromaDB, Qdrant ou Pinecone). - Construção de pipelines de busca semântica, recuperação de informação e aplicações com RAG: - Desenvolver pipelines que combinam: consulta → embedding → busca vetorial → re-ranking (opcional) → uso do contexto recuperado em aplicações com RAG. - Avaliar a qualidade da recuperação e das respostas geradas em cenários de NLP e RAG, utilizando métricas como recall@k e MRR. - Adaptação e experimentação com modelos em PyTorch ou TensorFlow: - Utilizar modelos pré-treinados e adaptá-los para tarefas específicas, como classificação, similaridade e extração de informação, com ajustes simples quando necessário. - Experimentar com diferentes arquiteturas (transformers, redes neurais simples). - Documentação e versionamento: - Documentar pipelines, decisões técnicas e resultados de experimentos. - Utilizar Git para controle de versão do código. Benefits - Disponibilidade: 40h semanais - Duração: 12 meses - Bolsa Auxílio: R$ 9.000,00 - Atuação: Remota Company Description Conheça o INOVA TALENTOS: - Vídeo 1 - Vídeo 2

Role Description DLG builds LuxuryIQ, a market intelligence platform used by 80+ luxury brands. The platform ingests data from 50+ sources — social platforms, search engines, secondary markets, advertising networks — through BigQuery into client-facing analytics and AI-powered tools. The data infrastructure exists and runs in production. It needs an experienced engineer to take ownership, improve reliability, expand coverage, and enforce quality standards across the full pipeline. What makes this different: - Your clean, documented data will power a conversational AI layer (via Model Context Protocol) and a SaaS solution that lets luxury brand CEOs query market intelligence in natural language. - You’re not building another analytics pipeline — you’re building the foundation of an AI product. SCOPE - You own the data lifecycle from ingestion to delivery. - You hand off clean, documented data to the platform engineering team. - Clear boundary: you own everything up to the handoff; platform engineering owns what happens after. - You own the budget for the data function (vendors, infrastructure, tooling). Ingestion: - Build and maintain crawlers, API integrations, and vendor data feeds. - Monitor collection reliability. - Onboard new data sources within planned timelines. Transformation: - Manage the full pipeline (from raw to production). - Implement automated QA at every stage. Delivery: - Maintain schema integrity in BigQuery (primary), PostgreSQL, and regional databases (China). - Ensure downstream consumers — including the MCP/AI layer — receive consistent, queryable, documented datasets. Documentation: - Document every transformation rule, edge case, and business logic decision. - The goal: any question about data behaviour can be answered from the spec, not from someone’s memory. Requirements - SQL fluency (BigQuery preferred) - Python for pipeline orchestration, transformation, and validation - Data modelling and schema design for multi-source, multi-market datasets - Web scraping, crawler development, and API integrations - Entity resolution and deduplication across heterogeneous data sources - Working knowledge of AI/LLM tooling (Claude Code, Copilot, or equivalent) Benefits - Full-time remote position - Regular travel to Geneva - Employment via Employer of Record Ideal Background - You’ve worked with scraped, unstructured, messy real-world data at scale. - You know what it’s like to reconcile the same entity across dozens of sources with different naming conventions, missing fields, and inconsistent formats. - Strong-fit industries: Price comparison and travel aggregation platforms, marketplace intelligence and web analytics, real estate platforms, alternative data providers for finance, or competitive intelligence companies. - The key differentiator: Have you built a data product that external customers pay for? - Interest in the luxury industry is a plus but not required — the data engineering challenges are universal.