• The person in this position is responsible for the development of program logic models, supporting measurement plans, and impact metrics. • They will partner with program teams to execute strategies, aligning on measurement plans, and evaluating programs that support the ASPCA’s goals to improve the lives of animals. • They will conduct in-depth studies across ASPCA programming to generate credible measures of the programming’s current performance and to determine what efforts are most effectively and efficiently resulting in desired impacts. • As a member of the Strategy team, the Director, IMDS will lead the design and development of program learning logic models and program evaluations in collaboration with ASPCA program teams, subject matter experts, external partner organizations and other external stakeholders. • In addition to strong program evaluation, research, and analytical skills, this position requires an individual with a collaborative work style, experience in project management, and the ability to lead teams in a matrix structure, often without direct authority.

• Design and implement AI features in strategic products. • Build LLM-based solutions for use cases such as customer support, triage, recommendation, information generation/extraction, and workflow automation. • Develop and orchestrate multi-agent architectures for complex tasks. • Contribute hands-on to experimentation, architectural decisions, and the design of ML and AI solutions for the People BU's products. • Create AI services and production pipelines (APIs, observability, logging, performance monitoring). • Define and maintain quality metrics, conduct experimentation (A/B or controlled tests), and drive continuous improvement. • Help ensure security, privacy, and compliance in the use of sensitive data, including best practices for anonymization and governance. • Document technical decisions, trade-offs, and patterns to help scale AI adoption across the People BU.

• 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