• Define and help execute Trustwell’s applied AI strategy across product, engineering, data, and internal business operations. • Lead the implementation of AI capabilities that are practical, measurable, secure, and aligned to business outcomes. • Establish AI governance practices, including usage policies, risk controls, evaluation standards, approval processes, and ongoing monitoring. • Partner with Engineering to design repeatable AI implementation patterns for LLMs, agents, retrieval-augmented generation, structured outputs, model evaluation, observability, and production operations. • Partner with Product to identify, evaluate, and prioritize AI-enabled product opportunities that improve customer value and operational efficiency. • Develop frameworks for assessing AI use cases, including feasibility, risk, data availability, implementation complexity, cost, and expected business impact. • Create and maintain standards for responsible AI usage, including data handling, prompt management, model selection, explainability, auditability, and human-in-the-loop controls. • Build and guide AI evaluation processes, including test datasets, regression testing, hallucination detection, quality scoring, accuracy measurement, and production feedback loops. • Help establish internal AI adoption processes, including development workflows, engineering enablement, training, approved tooling, and practical usage guidelines. • Work with Security, Legal, Compliance, and IT stakeholders to ensure AI implementations align with Trustwell’s data protection, privacy, contractual, and security obligations. • Analyze structured and unstructured data to identify opportunities for automation, prediction, classification, summarization, enrichment, and decision support. • Develop prototypes, proofs of concept, and production-ready analytical or AI workflows where appropriate. • Provide technical leadership on model selection, vendor evaluation, build-versus-buy decisions, AI cost management, and long-term platform strategy. • Collaborate with data engineering and application teams to improve data readiness, data quality, metadata, lineage, and retrieval strategies for AI-enabled systems. • Define metrics and reporting to measure AI adoption, performance, quality, cost, risk, and business impact. • Act as a trusted advisor and mentor to engineering, product, and business teams as they adopt AI responsibly and effectively. • Other duties as required.

Role Description At Hertz, we are at the forefront of innovation, leveraging cutting-edge technologies to build the best damage management technologies in the transportation industry. We want the brightest AI & ML minds to join our Damage Science Team to help us develop and/or maintain capabilities. Examples of projects the team work on include: - Rationalize Repair Estimates and Invoices with Large Language Models (LLMs): Implement sophisticated LLMs to make intelligent repair routing decisions, ensuring repairs are conducted efficiently and cost-effectively. - Forecast Repair Needs: Develop models to predict future repair & maintenance needs based on historical data and trends. - Optimize Decision Making: Create models to determine if we should keep/sell/salvage a vehicle. - Demand Planning: Forecast customer demand at a given location at a given time. What You Will Do: - Formulate the strategic and tactical steps to carry out the model development lifecycle end-to-end (i.e. problem statement definition, exploratory data analysis, feature engineering, model development / tuning, and model implementation). - Build and maintain descriptive, predictive, and prescriptive models to measure the performance of the new products and services. - Define and implement best practices to generate accurate analytics, reports, visualizations, and dashboards to explain results simply and succinctly to technical, non-technical, and senior management. - Build partnerships and work cross-functionally to identify use cases and opportunities to enhance operational efficiency and drive business value through positive impact on OKRs. - Work with an owner mentality to drive business impact even if that means supporting pipeline creation or decision support analytics. Qualifications - 3-5 years hands-on experience in a data scientist role. - 3+ years of data querying languages (e.g. SQL) and scripting languages (e.g. Python). - 3+ years of end-to-end machine learning model development experience (e.g. problem statement definition, exploratory data analysis, feature engineering, model development / tuning, and deployment). - Demonstrated experience using machine learning to drive a business impact. - Experience in a ML or data scientist role with car rental or technology company. - Bachelor or Masters degree in Computer Science, or other quantitative discipline such as statistics, mathematics, physics or engineering. Benefits - Up to 40% off any standard Hertz Rental. - Paid Time Off. - Medical, Dental & Vision plan options. - Retirement programs, including 401(k) employer matching. - Paid Parental Leave & Adoption Assistance. - Employee Assistance Program for employees & family. - Educational Reimbursement & Discounts. - Voluntary Insurance Programs - Pet, Legal/Identity Theft, Critical Illness. - Perks & Discounts – Theme Park Tickets, Gym Discounts & more. Company Description The Hertz Corporation operates the Hertz, Dollar Car Rental, Thrifty Car Rental brands in approximately 9,700 corporate and franchisee locations throughout North America, Europe, The Caribbean, Latin America, Africa, the Middle East, Asia, Australia and New Zealand. The Hertz Corporation is one of the largest worldwide airport general use vehicle rental companies, and the Hertz brand is one of the most recognized in the world.

• Train and optimize Deep Neural Network architectures for Regression and Industrial Control use cases • Design and implement end-to-end Machine Learning and Deep Learning pipelines including data ingestion, data cleaning, feature engineering, model training, deployment, and monitoring • Work with large and complex datasets for preprocessing, exploratory data analysis (EDA), and feature engineering • Apply Regression Models and Predictive Analytics to solve business problems • Develop Time Series and Forecasting models for trend prediction and risk estimation • Collaborate with cross-functional teams to integrate ML solutions into production environments

Role Description As a Lead Data Scientist for our Fraud team, you will be on the front lines of protecting our company and our customers. You will leverage your expertise in machine learning, statistics, and data analysis to design, build, and deploy sophisticated models that detect and prevent fraudulent activity in real-time. This is a high-impact role where you will see your work directly translate into protecting millions of dollars and ensuring a trustworthy platform for our users. Responsibilities - Technical Leadership & Strategy: Define the long-term machine learning strategy for the fraud team, establish technical best practices, and mentor junior data scientists. - End-to-End Model Development: Own the entire lifecycle of fraud detection models, from data exploration and feature engineering to model training, validation, deployment, and monitoring. - Credit & Lending Fraud Mitigation: Design and develop models specifically targeted at lending fraud typologies, including synthetic identity fraud, first-party loan default fraud, and application fraud. - Advanced Analysis: Conduct deep-dive investigations into emerging fraud patterns and user behavior, using clustering, outlier detection, network analysis, and other unsupervised techniques to uncover hidden risks and organized fraud rings. - Experimentation: Design and execute A/B tests to measure the impact of new models, rules, and strategies on both fraud detection rates and user experience. - Stakeholder Collaboration: Partner closely with Product, Engineering, Risk, and Operations teams to translate business needs into data science solutions, seamlessly integrate ML scores with rule engines, and communicate complex results to non-technical audiences. - Productionalize Models: Deploy, monitor, and maintain machine learning models in a cloud environment, ensuring high availability and performance. - Reporting & Visualization: Build and maintain dashboards using tools like Tableau or Looker to track key performance indicators (KPIs) like fraud loss rates, false positive rates, and model performance. Qualifications - 5+ years of experience in a hands-on data science role, building and deploying machine learning models. - Proven experience leading complex data science projects from inception to production, including setting technical direction and guiding peers. - Expert-level Python for data analysis and modeling (pandas, scikit-learn, etc.). - Advanced SQL skills for complex data extraction and manipulation. - Deep experience with tree-based ML models (XGBoost, CatBoost, LightGBM) and statistical models (Logistic Regression, Lasso/Ridge). - Deep understanding of model explainability frameworks (SHAP, LIME) and algorithmic fairness to ensure models comply with credit lending regulations. - Strong understanding of sampling techniques for handling highly imbalanced datasets. - Practical experience with clustering and outlier detection techniques (e.g., K-Means, K Nearest Neighbors, Isolation Forest). - Proven experience with the full modeling lifecycle, including model deployment, monitoring, and maintenance on a cloud platform like GCP, AWS, or Azure. - A solid foundation in statistics and experience designing and analyzing A/B tests. - Excellent stakeholder management and communication skills, with a demonstrated ability to explain complex technical concepts to diverse audiences. Advanced English level. Requirements - Direct experience in a FinTech, payments, or risk/fraud-focused role, particularly with exposure to credit or consumer lending. - Experience working with traditional credit bureau data (Experian, Equifax, TransUnion) and alternative credit/identity data sources. - Experience with Graph Neural Networks (GNNs) or graph analytics tools (e.g., Neo4j, NetworkX) to map complex fraud networks. - Familiarity with consumer lending regulations (e.g., FCRA, ECOA) and their impact on machine learning model development. - Hands-on MLOps experience (e.g., CI/CD for models, versioning, automated retraining). - Experience with Google Cloud Platform (GCP), especially Vertex AI. Benefits - Competitive salary - Initial stock options grant - Annual performance bonus - Health, dental, and vision plans - Remote work environment, although we have offices in Miami and México City and would love to work in hybrid model if you are up to it. - Continuous learning opportunities - Unlimited PTO - Paid parental leave - Empowering opportunities for growth in a dynamic entrepreneurial environment Equal Opportunity Employer At Félix, we are committed to providing equal employment opportunities to all qualified employees and applicants without regard to race, religion, nationality, sex, sexual orientation, gender identity, age, or disability. This policy applies to all terms and conditions of employment, including recruitment, hiring, placement, promotion, training, compensation, benefits, and termination.