RUDY MARTIN

• Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA): Developed advanced models to evaluate the impact of paid, earned, and owned media on customer acquisition, retention, and sales. Analyzed the effectiveness of marketing channels such as TV, digital, social media, and email marketing through MMM and MTA techniques. Used both internal and external (OMD, Google Analytics, Adobe CJA)
• Channel Contribution Analysis: Quantified the contributions of various marketing channels to sales outcomes, applying techniques like time-series modeling, regression analysis, and uplift modeling. Conducted scenario analysis to simulate the potential outcomes of different budget allocations.Worked on over 50 different A/B and multivariate experiments to determine next best offer, user engagement and sales effects.
• Attribution Across Touchpoints: Built and optimized multi-touch attribution models to assign credit for conversions across customer touchpoints. Leveraged machine learning algorithms to attribute sales and conversions accurately, informing budget reallocations across digital and offline media channels. This included first-touch, last-touch and other custom departmental allocation algorithms.
• A/B Testing and Experimentation: Designed and executed A/B tests to validate MMM and MTA models, testing variations in channel mix and creative assets to measure their impact on performance. Improved test-and-learn methodologies to refine marketing strategies in real-time. Used both vendor-supplied portal and custom designs for experiments. Included additional approaches such as sentiment analysis, survivor decay and other ML approaches.
• Reporting and Visualization: Created dashboards in Tableau, Looker, and PowerBI to present MMM and MTA results, enabling stakeholders to understand ROI and optimize marketing spend across channels. Visualized key insights for paid, earned, and owned media in real-time to support data-driven marketing decisions. Built Generative BI reports using customized Dataflows.
• Collaboration with Marketing Teams: Partnered with product managers, media planners, and marketing analysts to ensure alignment on marketing objectives and refine data-driven strategies based on MMM and MTA insights.

5+ years identifying millions in incremental revenue and net savings for one of the world's largest banks through 50+ experiments and A/B tests, covering design elements, product offers, and cybersecurity features.

• Hypothesis Development: Partnered with product teams to define testable hypotheses, focusing on high-impact areas like product adoption and reducing onboarding friction. Leveraged prior data analysis to ensure tests targeted key business issues.
• Exploratory data analysis (EDA) on high-dimensional datasets. Derived valuable insights from large, complex data sets to inform business decisions, find research areas or build prototypes for Cloud platforms.
• Experimental Design: Used randomization or stratified sampling to split traffic and minimize bias. Tailored experiments to user segments based on demographics and behaviors. Applied multivariate testing to analyze variables like UI, copy, and product features.
• Statistical Tools & Techniques: Employed t-tests, ANOVA, and Bayesian inference to compare variations, ensuring 95%+ confidence. Used power analysis to determine sample sizes, avoiding type I/II errors. Automated data extraction and reporting with PySpark, Python, R, and SAS for faster iterations.
• Feature Engineering: Suggested feature engineering techniques to enhance propensity model performance, incorporating GCP domain knowledge into modeling
• Data Collection & Analysis: Integrated Google Analytics, Oracle, Hadoop, Teradata, and BigQuery to track KPIs. Monitored uplift modeling to assess the impact on specific user groups, and developed dashboards in Tableau, Looker, ThoughtSpot, and PowerBI for real-time stakeholder insights.
• Test Execution & Monitoring: Monitored live tests using real-time analytics pipelines, pausing when necessary to mitigate risks or capitalize on early results.
• Post-Test Analysis & Refinement: Conducted post-test analyses, focusing on significance, lift, and long-term business impact. Iterated using feedback loops to refine models, avoiding short-term gains at the expense of long-term value.
• Business Integration: Collaborated with development teams to scale successful tests across the user base. Conducted meta-analyses to detect patterns, informing strategic business decisions. Presented findings to leadership, translating complex statistics into actionable insights.
• Fairness Research: Led AI/ML fairness research using reduce fraud risk by identifying anonymous visitors.
• Impact Insights: Influenced business decisions through data-driven insights and presentations, driving digital transformation and marketing strategies.
• SME Projects: Served as SME for large-scale cloud migrations (Teradata, Oracle, Hadoop to GCP), OMD data integration, ThoughtSpot AI analytics, Google Analytics Hub rollouts, Adobe CJA architecture, Fargo chatbot localization, and other ad hoc projects.
• Mentoring and POCs: Trained other team members in best practices for data science methodologies and tools for working with LLMs such as prompt engineering and specialized libraries for multi-dimensional or multi-model data, such as computer vision (OpenCV, PyTorch, TensorFlow) to foster a culture of collaboration and continuous learning. Leveraged LLMs and SLMs like Mistral and Gemini on GCP’s Vertex AI for internal POCs.

Improved lead generation by 18% for a leading network security and testing hardware manufacturer:

• Used machine learning algorithms that optimized user engagement on a 5,000-page website.
• Applied predictive analytics and NLP techniques to enhance online content quality and optimize network traffic analysis across a complex of multilingual sites.
• Participated in requirements meetings to coordinate marketing campaigns across different channels, or improving marketing tech workflows.
• Integrated HubSpot and Salesforce lead forms.
• Developed a Looker dashboard with data sourced from Google Analytics to effectively communicate business insights, enhance site reliability, and ensure data quality by identifying and addressing inconsistencies and errors in datasets.
• Experience working with Azure and AWS services.

12 years providing quantitative data and market research services to financial and publishing firms:

• Led development and deployment of models for time-series analysis and financial data modeling, optimizing performance for large multi-billion institutional portfolios, while playing a key role in pre-sales efforts to generate business opportunities.
• Managed cross-functional teams in architecting, implementing, and continuously improving scalable solutions on cloud platforms, enhancing trade signal generation and delivering data-driven market analysis that informed strategic decisions for investment companies.
• Worked with data clients including Citadel, Two Sigma, and other quant hedge funds.

• Developed a multi-factor, multi-modal model for stock and investment research using SQL-to-text extraction and advanced time-series techniques. The product was widely adopted by Wall Street firms for automating financial analysis.
• Applied NLP techniques to extract and summarize financial data from unstructured text, delivering precise insights for investors.
• Successfully commercialized this model, enabling sophisticated financial strategies and decision-making across the investment sector.
• Lead a team of analysts and developers for the product series.