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Senior Staff Machine Learning Engineer at Spotify
I hold a B.A. in Economics from Bates College (2010) and a M.A. in Statistics from Yale University (2014) and specialize in building large-scale applied machine learning solutions across various application areas. Specialties: Machine Learning, Data Engineering, Computational Statistics, Statistical Analysis, Numerical Analysis, Data Science Programming Languages & Related: Python, Scala, Java (working knowledge), C (working knowledge), CUDA (working knowledge), SQL (multiple types), MatLab/Octave, FORTRAN (77, 95), BASH, R Tech-stack, Data-Tools, ML-Tools: DataFlow (Scio, Apache Beam, Python Beam), Scalding, Kubernetes, Docker, BiqQuery, Hadoop (basic system tasks, simple config manipulation), Kubeflow, Spark, Tensorflow, TFX, TFP, Pytorch, Stan, PyStan, PyMc, Scikit-Learn I have experience with both AWS (EC2, S3, Kinesis, RedShift) and GCP (GCE, DataFlow, GCS, BigQuery)
Yale University
Master of Arts (MA), Statistics
January 1, 2013 – January 1, 2014
Bates College
Bachelor of Arts, Economics
January 1, 2006 – January 1, 2010
Spotify
Senior Staff Machine Learning Engineer
March 1, 2022 – Present
Spotify
Staff Machine Learning Engineer
August 1, 2018 – March 1, 2022
Spotify
Senior Machine Learning Engineer II
January 1, 2017 – August 1, 2018
Spotify
Senior Machine Learning Engineer
May 1, 2016 – February 1, 2017
DigitasLBi
Data Scientist
June 1, 2014 – May 1, 2016
Greater Boston Area
DigitasLBi
Data Science Consultant
September 1, 2013 – June 1, 2014
Greater Boston Area
DigitasLBi
Senior Analyst, Strategy & Analysis - Advanced Analytics
July 1, 2012 – September 1, 2013
Greater Boston Area
Digitas
Analyst, Strategy and Analysis - Advanced Analytics
January 1, 2011 – July 1, 2012
Greater Boston Area
Industrial Economics
Research Analyst
June 1, 2010 – January 1, 2011
Cambridge
State Street Corporation
Intern - Investment Management Team
June 1, 2009 – August 1, 2009
State Street Corporation
Intern - Investor Services
June 1, 2008 – August 1, 2008
ML Stack for Media Planning, Targeting and Activation System
December 1, 2014 – Present
- scalable, parallelized ML system focused on binary classification problems (python, spark, scikit) - sequence analysis, path simulation (python, RLpy, RNN) - imputation system that leverages EM-PCA, Laplacian Eigenmaps, Stacked Denoising Autoencoders
Brand Connectivity Network
August 1, 2014 – Present
Created (O(n)) algorithm to propagate a network graph describing brand connectivity using data obtained from the facebook graph API. The algorithm develops a localized edge set which is then surfaced to igraph. We also applied Dynamic Network modeling methods to this same data. The graph propagation algorithm is written in C and incorporated into R via Rcpp. Analysis of the graph itself is done with the igraph library as well as tergm and Rsiena.
Ongoing Personal Projects
January 1, 2013 – Present
I like to keep busy. In whatever spare time I have, I explore new programming languages and Machine Learning/Statistics/Math techniques.
Cookie data processing system for Attribution
May 1, 2012 – April 1, 2013
The process of collecting user level cookie data is a crucial piece to any attribution system. We built a distributed system leveraging AWS's EMR offering to process our cookie files and run the model. With this system, we can run attribution daily.
Multi-Touch Attribution
February 1, 2012 – Present
Built a multi-touch digital attribution system that draws on a combination of rules-based and model based calculations. The modeling solution leverages Markov chains to assign attribution to specific touch points.
Predictive modeling for financial services client
January 1, 2011 – April 1, 2012
Built response and propensity models and carried out CLTV analysis for one of our financial services clients.
Cultural Fit Analysis
The candidate's extensive experience in a leading tech company like Spotify, coupled with their academic background and diverse project portfolio, suggests a strong fit for a dynamic, innovation-driven culture. Their interest in 'exploring new programming languages and Machine Learning/Statistics/Math techniques' aligns with a culture of continuous learning and technical curiosity. The progression through various roles and companies indicates adaptability and a commitment to career growth. The projects demonstrate a blend of theoretical understanding and practical application, which is valuable in a research-oriented yet product-focused environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven individual who explores new technologies and techniques in their spare time. Their progression through various roles at Spotify, from Senior ML Engineer to Senior Staff ML Engineer, suggests strong leadership potential, problem-solving abilities, and a capacity for continuous learning and growth. The descriptions of managing deadlines, workflow, and training junior staff at DigitasLBi also point to strong operational and team collaboration skills.