• Design, build, and deploy machine learning and AI-driven models to forecast revenue, optimize marketing and sales performance, and identify financial trends • Perform statistical modeling, feature engineering, and hypothesis testing to uncover key business drivers • Evaluate model performance through A/B testing, backtesting, and performance monitoring • Partner with the Data Engineering team to ensure scalable data pipelines and robust data quality using Snowflake, Rivery, and dbt • Develop reusable analytical frameworks, automated pipelines, and reproducible modeling processes • Maintain documentation and follow data governance best practices • Build dashboards and predictive analytics in Domo to surface insights for executives and business stakeholders • Collaborate with Product, Finance, Marketing, and Operations to translate business questions into measurable, data-driven outcomes • Support the VP of Data Engineering in building and operationalizing a high-performing data science environment • Research and prototype new AI and ML techniques (e.g., LLMs, generative AI, anomaly detection) • Stay up to date with the latest in machine learning, data science, and cloud analytics tools to enhance the company's capabilities

• Design and analyze experiments to improve revenue • Classify audience and content to support programmatic direct and indirect sales • Deliver evolutionary floor price guidance • Visualize complex systems • Build deep understanding of data generating processes

• Identifying and qualifying leads for AI/ML projects across various industries. • Architect and develop end-to-end AI/ML solutions tailored to complex business challenges • Leading client engagements from initial contact through project scoping and proposal development • Lead technical design and implementation of production-grade models and pipelines. • Own the deployment, optimization, and monitoring of models and infrastructure (MLOps). • Collaborate across teams to ensure technical excellence and project success. • Mentor peers and contribute to knowledge-sharing across the organization. • Driving sales strategies and achieving revenue targets. • Building long-term relationships with clients and partners.

• Participating in the design and development of AI solutions for challenging projects. • Building production level ML/AI solutions, with solid software engineering and ML/AI principles. • MLOps Automated deployment and monitoring (models and infrastructure). • Data analysis (data cleaning, variable transformation, etc.). • Developing and training ML models. • Putting AI models into production. • This means parallelizing, optimizing, tuning, testing the models to deploy in a production environment.