I help companies make data driven decisions


Data Strategy

Turning data into profit



Machine Learning, Econometrics, AI



Data Science, Data Engineering, APIs, Front-End


KPIs & Decision Making

Continuous Measurement and Optimization


What I do

I specialize in developing data driven business strategies with clearly defined KPIs, and building interdisciplinary technology teams and data driven decision frameworks.

I am a “full stack economist” experienced in implementing data engineering solutions and writing production ready data pipelines.


Where I work

I currently work as Senior Economist for Risk Modeling at Zillow. I am also an Adjunct Professor of Econometrics at the University of Southern California where I will teach graduate Big Data Econometrics.



I also continue to pursue research with leading academic economists. My primary areas of interest are causal inference using big data, modeling of spatial and network data as well as financial time series.

Unique Skillset

As a PhD economist, I am trained in applying mathematical rigor and economic modeling (together with common sense) to answer questions in a way which is meaningful to a business.

I am also a programmer who knows how to build data pipelines and technology infrastructure which can support deployment of models at scale.

This combination of skills gives me a unique vantage point when developing strategies for turning data into profitable and scalable products.

Areas of Expertise

Business Building:
Developing Data Driven Strategies, Decks & Fundraising, KPI Tracking & Optimization

Technical Areas:
AB Testing, Applied Microeconomics, Bayesian Statistics, Big Data, Causal Inference, Data Engineering, Econometrics, Machine Learning, Spatial and Cross-Sectionally Correlated Panel Data, Time Series


Adobe Creative Suite, Airflow, Apache Spark, AWS, Cypher, Docker, Elasticsearch, Google Cloud, Git, H2O, Hadoop, Hive, html, Impala, Keras, Kubernetes, Linux, Matlab, Maple, Neo4j, PostGis, Python, R, serverless frameworks, Shell Script, SQL, Stata, Tensorflow, TeX, WordPress, yaml


I hold a Ph.D. in Economics and M.Sc. in Quantitative Economics from the University of Southern California in Los Angles.

I also have a B.Sc. in Economics from the
University of Gothenburg and a B.Sc. in Mathematics from Linköping University in Sweden.


I have over 10 years of experience in using data modeling to answer real world questions. I have worked with leading technology startups in areas such as medicine, advertising, psychometrics, consumer services and real estate. Read more here.

Get in touch

Selected Projects

Double-Question Survey Measures for the Analysis of Financial Bubbles and Crashes

We develop a novel type of survey indicator which can be used to measure and forecast financial bubbles and crashes. We show how the application of our indicator improves the out of sample forecast of house prices across US Metropolitan Statistical Areas when compared to traditionally used methods.

Joint work with Hashem Pesaran

Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach

We propose a novel estimator of peer effects. Our estimator takes into account the fact that there may be unobserved characteristics which drive both link formation and behavior, which, if not taken into account, introduce bias into traditional types of peer effect estimators. We use a sieve semiparametric approach and establish asymptotics of the semiparametric estimator.

Joint work with Roger Moon

Estimation and Inference in Multivariate Spatiotemporal Models with Common Latent Factors

When analyzing processes which are correlated across space and time – such as house prices – it is important to take into account the fact that correlation might be induced by unobserved factors (common shocks) and “neighbor” effects (one unit affects another one). We propose a novel estimator which controls for both unobserved factors and spillover effects and apply it to forecasting house prices.

Joint work with Hashem Pesaran and Cynthia Fan Yang