Patrick Russell Email & Phone Number

Seasoned data science and marketing professional. I thrive at the intersection between data science / analytics, technology, and marketing. I think that with the right tools and access to better information that marketing teams improve in efficiency, creativity, and problem solving. Data Science techniques such as predictive models for lead conversion, customer churn, customer lifetime value, cohort analysis, product selection, and behavioral segmentation provide a solid means to that end. Web based companies collect vast amounts of data but turning that data into information you can operationalize and use takes a wide skill-set and a deep background in technology, analytics, and marketing. I've been building on that skill-set and background over the last 12 years and continue to build it. As a dedicated self-learner and I'm focused and studying and learning new techniques and technologies. Specialties: Business analytics, marketing analytics, and strategyCost modeling, predictive modeling, recommendation engines, customer segmentation, attribution modeling.Performance based online marketing models, automated managementTools and technology: Python, Numpy, Scipy, pandas, Matplotlib, scikit-learn, statsmodels, Dask, Bokeh, Ipython/Jupyter notebooks, Luigi, SQL (Postgres, Redshift, MySQL, Presto), Git, Spark, Jenkins, Drake, AWS, EC2, DynamoDB, Redshift, Kinesis, Lambda, RDS, S3, boto, AWS CLI, Google Analytics, Optimizely, Facebook analytics, Facebook Custom Audiences, Facebook APIs, Google adwords, Linux, bash, cron, vim, Excel.Data Science / Analytics techniques: Linear Regression, Logistic Regression, Survival and time to event analysis, "Buy til You Die / BG/NBD models, Random Forrest, k means and nearest neighbors clustering, Beta/Binomial models for A/B testing.Currently learning and or exploring: Clojure, Neural Networks, serverless, aiops

Listed skills include Analytics, Google Analytics, Python, Scipy, and 23 others.

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  Regis University

  Mathematics