Oliver W.

I help organizations transform complex data into actionable insights that drive growth, improve decision making, and create measurable business value. With a PhD in econometrics, statistics, and causal inference and more than a decade of quantitative experience, I specialize in data science, predictive modeling, survey research, marketing analytics, business intelligence, experimentation, and advanced statistical analysis.

My experience spans e-commerce, iGaming, insurance, banking, non-profit, automotive, telecom, retail, healthcare, engineering, and research sectors. I have delivered projects ranging from forecasting, predictive modeling, customer analytics, and large-scale survey research to marketing measurement, attribution, and end-to-end analytics platforms.

I have extensive experience working with survey and observational data, including questionnaire design support, survey data cleaning and validation, scale and index construction, missing data handling, exploratory analysis, statistical modeling, and results visualization. I have analyzed customer, employee, stakeholder, healthcare, and research survey data using techniques such as logistic regression, count models, multilevel models, Bayesian methods, and causal inference approaches, translating complex findings into actionable recommendations.

I have extensive experience building data-driven solutions using Python and SQL, including data analysis, machine learning, forecasting, statistical modeling, Bayesian analysis, survey analytics, customer segmentation, and decision-support systems. My expertise includes Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA), incrementality measurement, A/B testing, geo experiments, brand lift studies, causal inference, and Bayesian methods.

Experienced in developing Bayesian MMMs using PyMC-Marketing and Google Meridian, I help organizations understand the true drivers of business performance by quantifying channel contribution, adstock and saturation effects, ROI, and budget optimization opportunities. I translate complex analytical findings into practical recommendations that stakeholders can confidently act upon.

I have worked extensively with data from Google Ads, Meta, TikTok, LinkedIn, CRM systems, call-tracking platforms, survey platforms, web analytics tools, customer databases, and cloud data warehouses. My technical background includes HubSpot, Datacor, BigQuery, GA4, Snowflake, and custom internal data sources, with a strong focus on data integration, validation, ETL processes, and scalable analytics workflows.

Beyond analysis and modeling, I develop dashboards, visualizations, and decision-support applications using Streamlit, Looker, Plotly, and business intelligence platforms. I am passionate about making data accessible and useful, whether through executive dashboards, interactive analytics applications, or clear communication of complex statistical results.