• Realizar estudos e análises para identificar gaps, inconsistências e oportunidades de melhoria nos dados cadastrais. • Construir modelos estatísticos e preditivos para inferência, recomendação e enriquecimento de dados. • Desenvolver modelos de Golden Record, garantindo a melhor versão do cadastro do cliente. • Utilizar técnicas de grafos para identificar relações complexas e oportunidades no Cadastro, como detecção de vínculos, clusters e padrões de relacionamento entre entidades. • Trabalhar com grandes volumes de dados estruturados e não estruturados. • Aplicar técnicas de data profiling, imputação e enriquecimento de dados. • Colaborar com áreas de negócio e engenharia para melhoria contínua da qualidade dos dados. • Apoiar iniciativas de governança e projetos de MDM (Master Data Management). • Documentar metodologias, processos e resultados de forma clara e acessível.

• The Data Scientist at Earnest LLC in Oakland, CA will guide product recommendations and model development, and communicate analyses and insights to the Data team and broader organization. • Develop and maintain predictive machine learning (ML) models to assess potential risks and opportunities across Earnest’s lending products, contributing to the enhancement of risk and marketing assessment procedures. • Participate in the end-to-end ML pipeline, spanning from data collection to model deployment. This includes collaborating with data and infrastructure engineers to automate training and monitoring using BI tools and writing ETL pipelines to source data for model training from various sources such as Earnest or Navient data warehouses, production databases, third-party providers, and publicly available datasets. • Conduct experimentation and execute causal inference analyses on pricing, marketing, and product models to optimize product conversion rates. • Write code for inferential models that guide product and underwriting decisions, and document modeling decisions through Confluence or GitHub Readme files. • Collaborate closely with cross-functional teams and stakeholders to accelerate solution iteration and achieve measurable outcomes. • Articulate model impacts and analytical insights through presentations and reports, draft recommendations to optimize product and underwriting policies, and provide guidance and mentorship on modeling best practices to peers on the team.

• Own the Permissions & Privacy Framework – Serve as the single business owner of client data permissions and governance, defining role-based access principles and clear decision rights. Evolve the framework as appropriate • Operationalize Access & Policy Management – influence the design and manage workflows for access provisioning, deprovisioning, temporary access, advisor transitions, and confidential client handling • Govern Ongoing Permission Decisions – Act as the central project manager for complex access requests. Maintain clear policy guidance for recurring scenarios. • Build Audit, Monitoring & Risk Controls – Establish audit processes, reporting, and monitoring to ensure consistency and detect inappropriate access • Drive Cross-System Alignment – Partner with Product and Technology teams to standardize permissions across all platforms and products • Enable the Organization – Partner to create training, guidance, and FAQs to ensure employees understand access policies and processes.

• Understand sponsor data needs and be able to translate these requirements into necessary analytics • Proactively specify algorithms, models, analyses, and output required to meet sponsor needs • Draft business requirement documents and specifications. • Anticipate reporting and analytic needs to support sponsors • Understand CDISC standards and clinical trial submission processes and be able to produce data transfers and trial documentations to these standard independently • Confidence to present results and interpretations independently to sponsors • Ability to explain complex data concepts in an easily understandable way • Skilled at summarizing data in succinct, effective presentations and executive summaries. • Independently manage numerous data analytic projects and deliverables concurrently • Build, develop, and deploy data analytics, data models, reporting systems, data automation systems, dashboards and performance metrics. • Maintain reporting systems and dashboards. • Support the creation of data visualization tools. • Draft and implement Statistical Analysis Plans. • Monitoring of study data • Data transformation, transposition, cleaning, curation and reprocessing in support of data transfers and delivery to sponsors. • Implementation of CDISC data standards including SDTM and ADaM datasets. • Interact with sponsors to resolve scientific queries and participate in study planning. • Write clinical study reports, analytic reports, and other scientific reports. • Contribute to data curation and aggregation. Provide requirements and documentation of data structure and needs to support data analytics. • Analyze and report on cognitive outcome measures and cognitive tests. • Develop and QC statistical and programming output while maintaining rigorous quality standards. • Interact with project management, statistics, data management, statistical programming, and site services teams internally to support scientific analysis and reporting and support data related questions. • Develop solutions to unique customer requests relating to topics such as stratification, inclusion, reliable change monitoring, data monitoring, normative data, and other cognitive testing applications. • Generate clinical study report summary tables, data listings, figures, and other output as specified in the statistical analysis plan or other scientific reporting specification. • Review and monitor cognitive data rater performance, data monitoring findings, custom programming/statistical analysis, normative data, z-scores and computations. • Analyze and interpret cognitive data from clinical trials and research studies. • Critically evaluate internal processes and scientific methodologies. • Contribute to the development of processes, technology, and infrastructure which support the functions of scientific services and related operations. • When acting as a QC programmer, work independently from source data and ensure a 100% match of all output. • Maintain an intimate knowledge of Cogstate’s data systems. • Provide requirements for development and commercialization of new technology.