• Design, build, and operate the Decision Intelligence Engines that power Addi’s personalized customer journeys • Design and maintain segmentation models based on behavior, performance, lifecycle stage, and growth potential. • Design, train, and deploy models to predict customer behaviors and risks, ensuring outputs are interpretable and segment-aware. • Design and deploy LLM-based solutions for customer growth, treating them as production systems with strong guardrails. • Design, implement, and scale machine learning and ML models to analyze customer behavior, optimize marketing strategies, and improve overall engagement with Addi’s platform. • Collaborate with data engineering teams to design and optimize data pipelines that support the seamless deployment of the models into production. • Continuously monitor the performance of deployed models, evaluate their impact on business metrics, and iterate to improve their accuracy, scalability, and overall performance. • Work closely with product managers, marketing teams, and stakeholders to translate data insights into actionable strategies. • Continuously innovate by proposing ML models, algorithms, or tools that enhance customer experience, optimize product recommendations, and improve overall marketplace performance. • Design and execute A/B tests to assess the impact of different offers, product recommendations, and marketing strategies on customer engagement and conversion rates.

• Own the end-to-end architecture and roadmap for Montrose’s enterprise data and AI platform on Snowflake (Azure) • Lead the transition from legacy .NET and MS SQL–based data environments to modern cloud-native data patterns • Define and enforce standards for data ingestion, transformation, modeling, analytics, and AI enablement • Establish modern ELT pipelines using dbt, Fivetran, and/or appropriate Azure-native equivalents • Design the platform to be Data Cloud–ready, supporting future integrations with Salesforce Snowflake Data Cloud and Workday Snowflake Data Cloud • Lead the evaluation, selection, and implementation of enterprise BI and analytics tooling • Lead the evaluation and adoption of enterprise AI / LLM platforms (e.g., Abacus or comparable solutions) • Design and deliver an initial AI-driven use case that automates and materially improves testing, monitoring, and remediation workflows • Ensure the initial AI use case delivers measurable efficiency, reliability, or cost improvements that justify capital investment • Establish analytics-ready and AI-ready data models • Define data and AI governance practices including security, access control, lineage, and cost management • Serve as the senior technical authority for data, analytics, and AI platform decisions • Partner with IT, Engineering, Operations, Finance, and executive leadership • Build and lead the data organization, starting with a senior data engineer • Leverage a nearshore delivery model utilizing Latin American talent • Mentor engineers and analysts, setting expectations for rigor, documentation, and measurable outcomes

• Responsible for the company's Data department • Oversee data studies and analyses • Ensure compliance with agreed deadlines • Team development • Improve the product with a data-driven perspective • Understand the importance and urgency of client requests • Monitor prioritization progress • Propose improvements to outcomes • Organize the department's workflows • Participate in internal and external company alignments • Study and understand new types of fraud to generate insights and propose improvements

• Develop end-to-end AI solutions from discovery through deployment and monitoring, including modeling (NLP/ML/LLMs), APIs and integrations. • Design and evolve chatbots and conversational channels using NLP/NLU, driving continuous improvement. • Build integrations via APIs/JSON, microservices and event-driven architectures, and implement MLOps with pipelines, versioning and observability. • Analyze conversational data and recommend actions based on KPIs, ensuring quality through testing and validation. • Work in agile methodologies, collaborating with business, design, engineering and security teams.