Data Scientist

• Design, develop, and maintain analytical data models, focusing on dimensional modeling (star schema) to support dashboards and credit-area analyses.
• Build and optimize data pipelines using orchestration tools and distributed processing.
• Develop data ingestion, transformation, and validation routines in data lake and data warehouse environments.
• Partner with business, operations, and technology teams to understand needs and translate requirements into efficient data structures.
• Perform exploratory analyses, identify patterns, and support decision-making with data-driven insights.
• Document models, transformation rules, data dictionaries, and ingestion workflows.
• Support squads and stakeholders in creating KPIs, metrics, and analytical views.
• Monitor database performance and propose continuous improvements in architecture, governance, and data quality.

• Analyze data, build predictive models, and implement robust data-driven strategies to enhance risk management processes.
• Process and work with large-scale datasets to extract actionable insights and develop advanced algorithms to safeguard the ecosystem.
• Collaborate with a multidisciplinary team of risk analysts, data scientists, and software engineers.

Data Scientist – Risk

• You will transform demand, occupancy, and purchasing behavior data into models that price a network with thousands of origin-destination pairs and multi-leg routes.
• Conduct exploratory and statistical analysis to discover patterns, generate hypotheses, and guide modeling.
• Model demand and no-shows per origin-destination pair using gradient boosting and time series methods, correcting for censored demand bias (unconstrained demand).
• Estimate price elasticity and willingness-to-pay by segment and route as the basis for price optimization.
• Model seat protection and capacity allocation (fare buckets) on multi-leg routes, balancing load factor and revenue.
• Design A/B tests and causal inference analyses that validate price changes and measure real uplift, with clear statistical rollout criteria.
• Ensure interpretability (e.g., SHAP) to defend each price to the business and regulators.
• Design strategies for routes with limited historical data, starting from literature priors and evolving to ML solutions, on a per-tenant basis.

Senior Data Scientist

• Analyze structured and unstructured data to identify patterns of fraud and money laundering.
• Develop Machine Learning models (classification, clustering, anomaly detection) for financial security problems.
• Apply feature engineering, handle missing values, normalize data, and balance datasets.
• Ensure proper dataset separation and validation, avoiding data leakage.
• Create visualizations to communicate results to technical and non-technical audiences.
• Experiment with and compare algorithms (Scikit-Learn, LightGBM, CatBoost, TensorFlow).
• Query and explore data via SQL in distributed sources (Athena).
• Handle structured data (CSV, Parquet) and unstructured data (JSON, images).
• Prepare and transform data for modeling.
• Version experiments and models with MLflow and DVC.
• Monitor model performance in production (business metrics and ML metrics).
• Detect and address data drift and concept drift with corrective actions.
• Build and maintain model monitoring dashboards (Grafana or similar).
• Configure alerts for performance degradation and operational anomalies.
• Collaborate with the engineering team to define retraining and model versioning strategies.
• Research new ML techniques and algorithms applicable to the domain.
• Apply explainability (XAI) to support decision-making and regulatory compliance.
• Contribute to technical documentation and team best practices.
• Collaborate with engineering teams on model integration and deployment.

• Develop, train and optimize Machine Learning models to support decision-making in the Credit and Collections areas;
• Manage the model lifecycle (monitoring, performance tracking and recalibration);
• Conduct exploratory analyses, ad hoc studies and generate business insights;
• Identify and incorporate new data sources (structured and unstructured);
• Communicate results clearly to stakeholders while maintaining technical rigor;
• Promote a data and analytics culture across business areas;
• Encourage the use of AI and propose innovative approaches, challenging the status quo.

Data Scientist – Mid-level

• Design, develop, validate, and continuously enhance end‑to‑end risk models and scorecards across the credit lifecycle, including Originations, Pricing, Loss Forecasting, Servicing, and Collections.
• Partner closely with Risk, Finance, Product, and Operations teams to translate business problems into analytical solutions that drive measurable outcomes.
• Evaluate and integrate new data sources, features, and modeling approaches, assessing their incremental value, robustness, and impact on risk and profitability.
• Lead what‑if, stress testing, and scenario analyses to support strategic initiatives, new programs, and policy changes.
• Ensure models are interpretable, stable, and production‑ready, balancing predictive power with regulatory and operational constraints.
• Own the ongoing monitoring and performance tracking of models, identifying degradation, bias, or drift and proactively proposing improvements.
• Stay current on regulatory expectations and industry best practices in credit risk modeling, particularly within the auto finance space, and incorporate them into modeling standards and documentation.

Senior Data Scientist – Risk Analytics

• Lead the NPS strategy, ensuring continuous generation of actionable insights
• Manage and evolve survey tools (e.g., Qualtrics), including: structuring relationship cadences; sending multichannel surveys (especially WhatsApp/APIs); data governance and quality
• Develop and monitor customer experience and satisfaction metrics (NPS, CSAT, CES)
• Manage and integrate customer data from multiple sources (CRM, digital channels, surveys, internal databases)
• Monitor and analyze usage and customer behavior metrics on digital channels (e.g., visits, journeys, conversion, engagement)
• Connect experience data (NPS) with internal business indicators (e.g., conversion, retention, operational performance), producing correlational analyses and integrated insights
• Design and evolve a combined monitoring model that links experience indicators, customer data, and digital performance, ensuring a holistic view of the journey
• Build analytical models, segmentations, and predictive analyses to guide strategic decisions
• Apply and promote the use of Artificial Intelligence and advanced analytics (e.g., predictive models, NLP, text analytics) to automate analyses and support action planning
• Lead data automation and integration initiatives (e.g., Salesforce, APIs, internal databases)
• Translate data into clear analyses, executive storytelling, and practical recommendations for business areas
• Ensure governance, standardization, and consistency of databases
• Act as a bridge between business, technology, data, and customer experience teams

Data Manager, NPS

• Support the management and organization of the squad's activities and the product backlog
• Track requests, update status, and ensure smooth delivery flow
• Work closely with Development and Architecture teams to identify blockers and support their resolution
• Participate in agile ceremonies and rituals, promoting alignment across involved areas
• Gather, organize, and document relevant information for product evolution
• Ensure product documentation is up to date and accessible to teams
• Communicate initiative progress, share information, and collect stakeholder feedback
• Support backlog prioritization and maintenance, removing obsolete items and aligning requests with business value
• Analyze customer and user feedback to identify improvement opportunities
• Monitor product indicators and metrics, supporting quantitative and qualitative analyses for decision-making
• Contribute to continuous improvement initiatives for processes, products, and user experience

Product Analyst, Mid/Senior

• We are looking for a Product Manager to build and evolve NDD’s financial vertical, focusing on solutions related to a Payment Institution (PI).
• The person will be responsible for leading the strategy and evolution of financial products.
• Connect customer needs, business vision, regulatory requirements and technical capabilities.
• Lead discovery and delivery of financial products and digital solutions.
• Define and structure the vision, strategy and roadmap for the financial vertical’s products.
• Translate business objectives into prioritized, value-driven initiatives.
• Map market opportunities, competitors and trends in the financial sector.
• Work closely with multidisciplinary teams (technology, UX, compliance, operations and commercial).
• Define and track product metrics and performance indicators.
• Support the development of the financial vertical’s value proposition.
• Ensure alignment between regulatory requirements and product evolution.
• Facilitate ceremonies, refinements and strategic alignments with stakeholders.
• Participate in the preparation of business cases and feasibility analyses.
• Support decisions related to integrations, financial partners and the payments ecosystem.

Senior Product Manager

• Propor, desenvolver e implementar modelos de Machine Learning robustos, escaláveis e performáticos de ponta a ponta;
• Implementar modelos de recomendação e personalização;
• Desenvolver sistemas de inteligência híbridos, combinando Machine Learning e IA generativa;
• Liderar tecnicamente projetos e iniciativas, garantindo a entrega de soluções de alta qualidade;
• Interagir com áreas de negócio e de produto, para definir soluções através de uma visão sistêmica e abrangente;
• Conduzir análises complexas, extraindo insights valiosos e gerando impacto positivo nos negócios;
• Desenvolver e implementar soluções de monitoramento e mensuração de resultados;
• Otimizar o tratamento de dados massivos, garantindo eficiência e precisão;
• Apoiar outros cientistas de dados, promovendo um ambiente de aprendizado contínuo;
• Colaborar com equipes multidisciplinares para integrar soluções de dados em diferentes áreas da empresa;
• Manter-se atualizado com as últimas tendências e tecnologias em ciência de dados e IA, aplicando-as para melhorar continuamente os processos e modelos existentes.

Cientista de Dados Sênior

• Execute and evolve the entire lifecycle of Data Science models, working from data exploration through to the operation and maintenance of models in production.
• Architect scalable data and Machine Learning solutions, including complex pipelines, data versioning and automation via MLOps (CI/CD).
• Lead medium- to large-scale projects, coordinating technical deliveries and ensuring alignment between business requirements, data engineering and software teams.
• Act as a technical reference for Junior Data Scientists and analysts, providing mentorship, in-depth code reviews and supporting technical decisions.
• Influence the product roadmap and data initiatives, helping to prioritize projects with the highest business impact.
• Ensure the quality, stability and observability of models in production, managing technical risks and proposing continuous improvements.
• Create and disseminate technical standards, templates and documentation to increase the efficiency and maturity of the area.

Data Scientist, Mid-level

• Develop Data Science solutions: Create solutions that meet business needs, typically involving exploratory data analysis, machine learning models (supervised and unsupervised) and Operations Research (Optimization).
• Execute the full DS lifecycle: Lead from business understanding and exploratory analysis, through feature engineering, model building and performance validation, providing the necessary support to deploy developed models to production.
• Bridge with the business: Design, together with business areas, model usage and monitoring, ensuring clear communication to explain complex results to non-technical teams.
• Work in an agile team: Operate in a collaborative, multidisciplinary environment, handling cross-technical dependencies among peers with clarity and transparency.
• Explore AI innovations: Support the team in evolving solutions using new AI tools on the market for day-to-day work and for model explainability.

Data Scientist III

• Develop statistical, predictive and analytical models to project TPV (Total Payment Volume), revenues, costs and the main financial lines of the acquiring business.
• Analyze large volumes of transactional, financial and behavioral data to identify patterns, trends, risks and opportunities to improve business results.
• Build and evolve forecast models to support financial planning, performance monitoring and strategic decision-making for acquiring, acquisition and macro-products.
• Analyze variances between actuals, budget and forecast, explaining performance deviations and their main drivers, such as seasonality, customer behavior, product mix, acquisition channels and market dynamics.
• Develop segmentations of customers, products and channels to support growth, profitability, efficiency and commercial prioritization strategies.
• Work closely with product, finance, pricing, planning, data engineering and business teams to define assumptions, structure analyses and implement data-driven solutions.
• Translate complex business problems into analytical models, metrics, KPIs and actionable recommendations for stakeholders at different levels.
• Deliver complex insights clearly, executive-style and effectively to technical and non-technical stakeholders, supporting strategic decisions related to growth, profitability and financial efficiency.
• Build analytical views, dashboards and recurring studies to track TPV, revenue, costs, margin, profitability, customer acquisition and macro-product performance.
• Mentor and provide technical guidance to junior data team members, contributing to the analytical development of the area.

Product Specialist – Data Science

• Develop and manage pricing models for Distributed Generation (DG) products and the Free Energy Market
• Define discount policies by segment, channel, region, and customer profile
• Monitor margins, payback period, CAC, LTV, and profitability metrics
• Build price elasticity and discount sensitivity analyses
• Support negotiations with key accounts, the public sector, and strategic partners
• Ensure alignment between commercial strategy and company margin objectives
• Continuously monitor competitor activity, distributors, energy retailers/traders, and generators
• Map regulatory trends and their impact on prices, products, and competitiveness
• Produce market share analyses, market potential assessments, and regional attractiveness studies
• Identify opportunities for geographic expansion and new customer segments
• Build benchmarks for price, discount, churn, conversion, and commercial performance
• Develop executive dashboards to track commercial KPIs
• Analyze performance by channel: Inbound, Partnerships, Community/Referral (MGM), Regional Sales Executives, and Key Accounts
• Assess pricing impacts on conversion, CAC, churn, revenue, and asset utilization
• Generate recommendations to optimize channel mix and reduce CAC
• Support Budgeting, Forecasting, and Long Range Planning
• Build growth scenarios considering available energy capacity, occupancy/usage curves, competition, and regulatory evolution
• Prepare materials for Executive Committees, the Board, and Investors

Senior Product Analyst

• Coletar, tratar e analisar grandes volumes de dados estruturados e não estruturados;
• Desenvolver modelos estatísticos e de machine learning para previsão e classificação;
• Criar dashboards e relatórios para acompanhamento de métricas de negócio;
• Trabalhar em parceria com áreas de negócio para identificar oportunidades baseadas em dados;
• Implementar e monitorar modelos em produção;
• Garantir a qualidade e governança dos dados utilizados;
• Explorar novas técnicas e ferramentas de análise de dados e IA.

Cientista de Dados, Pleno

• Desenvolver soluções técnicas que atendam os requisitos de negócio, buscando a criação de aplicações escaláveis e performáticas;
• Desenvolver sistemas batch e APIs Rest em linguagem Python, baseado na documentação fornecida, criando aplicação que atendam às exigências do usuário;
• Realizar manutenção em portais web;
• Identificar, corrigir incidentes e realizar manutenção em sistemas, de acordo com a descrição do problema e a análise de logs;
• Elaboração de documentação técnica da solução implementada;
• Elaboração de testes unitários;
• Capacidade de interação com área de negócios com objetivo de elucidar requisitos de negócios, homologar sistemas desenvolvidos e contribuir com informações técnicas em reuniões de alinhamento da demanda;
• Desejável ter atuado com metodologias ágeis;
• Desenvolver e avaliar soluções baseados em IA generativa (LLMs) aplicadas a problemas reais de negócio, como recomendação de produtos, análise de documentos, chatbot e estratégias de cobrança;
• Conduzir experimentos para otimização de modelos, incluindo prompt engineering, fine-tunning e técnicas de RAG (Retrieval -Augmented Generation);
• Definir e implementar métricas de avaliação para modelos generativos (ex: qualidade, factualidade, consistência e viés);
• Realizar análises estatísticas robustas para validação de resultados, incluindo testes de hipótese, intervalos de confiança e simulações;
• Identificar riscos e propor soluções para mitigação de alucinações, vieses e problemas de confiabilidade.

Especialista em Ciência de Dados – IA Generativa

• Analyze structured and unstructured data to identify patterns of fraud and money laundering
• Develop machine learning models (classification, clustering, anomaly detection) for financial security problems
• Apply feature engineering, handle missing values, normalize data and balance datasets
• Prevent data leakage when splitting and validating datasets
• Create visualizations to communicate results to different audiences
• Experiment with various algorithms (Scikit-Learn, LightGBM, CatBoost, Keras, TensorFlow, PyTorch)
• Work with distributed data stores (Athena, Hive) processing large volumes
• Optimize SQL queries to reduce cost and execution time
• Handle structured data (CSV, Parquet) and unstructured data (JSON, images)
• Develop data transformation and preparation pipelines
• Create and maintain automated ML pipelines (training, validation, deployment)
• Containerize applications with Docker
• Manage pods and deployments with Kubernetes
• Ensure scalability and availability of solutions
• Monitor model performance in production
• Detect and remediate data drift and concept drift
• Configure alerts and metric dashboards (Grafana or similar)
• Implement automated retraining and model versioning
• Apply explainability techniques (XAI) for regulatory compliance
• Research new technologies and ML algorithms
• Contribute to best practices and technical documentation
• Stay up to date in Data Science and Machine Learning

Full Stack Data Scientist

• Develop and maintain marketing ROI and analytics models
• Analyze marketing and sales data to measure channel performance and advertising effectiveness
• Partner with business stakeholders to address questions, review model outputs, and identify key business drivers behind marketing performance results
• Work with Bayesian and linear models to evaluate marketing contribution and ROI
• Support and improve Streamlit-based applications used for reporting and visualization
• Build scalable Python-based data workflows and analytical tools
• Export and manage reporting outputs using Excel and related formats
• Collaborate with engineering and cross-functional teams to improve code quality and maintainability
• Work within AWS environments including S3 and EC2
• Monitor applications and workflows using DataDog

Marketing Data Scientist

• Compreender as demandas de negócio e traduzir em tarefas técnicas com apoio do time;
• Apoiar em operações de transformação e carga de dados (ETL/ELT);
• Apoiar na modelagem de bancos de dados e criação de fontes de dados;
• Analisar e interpretar dados utilizando técnicas estatísticas e aprendizagem de máquina;
• Comunicar resultados das análises e modelos a partir de relatórios técnicos e ambientes de visualização;
• Apoiar na construção e testes iniciais de modelos sob orientação;
• Seguir boas práticas de APIs de consumo de modelos;
• Participar da documentação e padronização de processos de análise;
• Colaborar com squads multidisciplinares;
• Apoiar na segurança e integridade dos dados utilizados e padronização de processos na esteira de MLOps;
• Executar atividades correlatas.

Cientista de Dados I – Foco em IA Generativa

• Act as a bridge between Product, Customer Success (CS), Support, Pre-sales, Sales, and Technology
• Perform initial assessment of requests, issues, and opportunities
• Organize and structure information to support the Product Manager's decision-making
• Triage items (enhancements, bugs, questions, customer requests, etc.)
• Prepare clear, concise summaries of requests
• Support organizing and running meetings (agenda, minutes, follow-up)
• Log and track requests in tools such as Azure DevOps
• Manage pending items, deadlines, and request statuses
• Consolidate information from different teams
• Assist the Product Manager with operational routines and backlog organization
• Participate in client meetings to gather context
• Ensure decisions and information are properly documented

See all jobs on JobTailor

Data Scientist

Data Scientist – Risk

Senior Data Scientist

Senior Data Scientist

Data Scientist – Mid-level

Senior Data Scientist – Risk Analytics

Data Manager, NPS

Product Analyst, Mid/Senior

Senior Product Manager

Cientista de Dados Sênior

Data Scientist, Mid-level

Data Scientist III

Product Specialist – Data Science

Senior Product Analyst

Cientista de Dados, Pleno

Especialista em Ciência de Dados – IA Generativa

Full Stack Data Scientist

Marketing Data Scientist

Cientista de Dados I – Foco em IA Generativa

Product Analyst Jr/Pl – Tax Solutions