Role Description As a Senior Data Scientist at R2, you will sit at the helm of R2 by analyzing large data from some of the leading technology platforms in the world, and deploying clever and scalable data-driven solutions that enable new financial opportunities to millions of small businesses across Latin America. Your solutions will drive critical business decisions in an automated and scalable way. - Lead forecasting initiatives by designing and implementing advanced time series models to predict sales and behavioural trends for thousands of customers. - Develop scalable machine learning solutions that power real-time decision-making across risk management, fraud detection, and product personalization. - Collaborate cross-functionally with product managers, engineers, and business stakeholders to translate complex data challenges into actionable insights and measurable business outcomes. - Drive innovation in fintech applications by experimenting with cutting-edge approaches (e.g., deep learning architectures, probabilistic forecasting, and transformer-based models). - Ensure compliance and transparency in model development, aligning with industry regulations and ethical standards for financial data usage. - Shape the data science strategy by identifying opportunities where predictive modeling can unlock new value streams and competitive advantages. Qualifications - At least 5 years of experience with machine and deep learning in a practical setting. - Good understanding of fintech products and risk management to interpret business data effectively. - Strong foundation in probability, statistics, and econometrics. Requirements - Strong expertise in time series forecasting methods based on statistical analysis (ARIMA, SARIMA, SARIMAX, VAR, exponential smoothing, or state-space models). - Expertise in Machine & Deep Learning (Random Forest, XGBoost, RNNs, LSTMs, or Transformers). - Knowledge of Bayesian theory (BSTS, Prophet, or ensemble forecasting). - Deep knowledge of machine learning techniques for sequential data (RNNs, LSTMs, GRUs, Transformers). - Strong proficiency in ML/DL frameworks in Python (e.g. Tensorflow, PyTorch, Scikit-learn). - Comfortable consuming data through APIs, SFTP, or straight-up CSVs. - Experience with explainable AI, especially in the context of Deep Learning Forecasting time series methods. Leadership & Business acumen - Data-oriented mindset: care about making decisions based on data. - Stakeholder management experience, keeping everyone up-to-date with key findings and explaining results, methodologies, and processes for data-driven decision making in a non-technical way.

• Designing and building the data Ingestion Pipeline • Designing, building and maintain large, complex data processing pipelines using Databricks and Azure Data Factory in Azure to meet enterprise Data requirements • Providing operational and functional support on creating, storing, managing, and maintaining enterprise data • Identifying, designing, and implement internal process improvements • Building the infrastructure required for extraction, transformation, and loading of data from a wide variety of cloud and on-premises data sources • Working with stakeholders including to assist with data-related technical issues and support their data infrastructure needs • Working closely with data architecture, data governance and data analytics teams to ensure pipelines adhere to enterprise standards, usability, and performance

• Drive ownership - Own and evolve the existing structured financial data taxonomy library at AlphaSense, inclusive of all client-facing labels, metric definitions and dataset classifications, ensuring both backwards compatibility and future proofing. • Set the standard - Understand existing naming standards and define how label conventions and governance principles apply across a growing range of data types, ensuring clients can intuitively discover, understand, and leverage the data Alpha-Sense has to offer. • Collaborate cross-functionally with engineering, data, product, sales, and client-facing teams to align stakeholders on taxonomy decisions and translate conceptual frameworks into scalable, production-ready systems • Serve as the internal subject matter expert on data semantics, and develop and maintain official internal and external documentation ensuring taxonomy standards are clearly articulated and accessible to all stakeholders • Proactively identify gaps and inconsistencies as new datasets are onboarded, and drive resolution in a structured and scalable way • Design and maintain taxonomy and data structures with AI and LLM compatibility as a core consideration, ensuring labels, definitions, and conventions support model training, inference, and broader AI-driven automation • Lay the foundation for a future taxonomy practice — building repeatable frameworks and positioning this function for team growth over time

• Develop differentiated messaging for thermal control systems for AI products (e.g., GPUs, ASICs, AI accelerators, SoCs, CPO, pluggable transceivers) • Articulate value across performance, power efficiency, and thermal design considerations • Translate complex concepts such as optical transceiver laser thermal control, AI server and GPU cooling, rack density, cooling requirements, and energy efficiency into customer-impact narratives • Lead launches of silicon and AI platform solutions optimized for high-density, thermally constrained environments • Develop GTM strategies that highlight performance-per-watt, cooling efficiency, and infrastructure readiness • Partner with product and engineering teams on positioning tied to data center scalability and sustainability • Analyze trends across AI infrastructure, high-performance computing (HPC), and data center thermal management • Track competitive approaches to liquid cooling, immersion cooling, advanced air cooling, and energy-efficient architectures • Develop insights on how competitors address heat density, power consumption, and AI cluster scaling challenges • Equip sales teams with materials explaining thermal tradeoffs, rack-level constraints, and cooling requirements • Develop tools to communicate TCO, power usage effectiveness (PUE), and data center operational efficiency • Collaborate with ecosystem partners including data center operators, cloud providers, and cooling and infrastructure vendors (e.g., liquid cooling solutions) • Create content on AI infrastructure challenges, including power delivery, cooling, and scaling constraints • Publish whitepapers and technical briefs on AI workload-driven thermal demands, cooling technologies (liquid cooling, immersion, direct-to-chip), sustainable AI infrastructure, etc. • Represent the company in discussions on AI data center design and efficiency trends