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IndustryMusic/Audio
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Engineering

Data Scientist - Machine Learning, Algorithims

at BeatsMusic in CA   —   Jul 16, 2014   |  
Overview
Us

We are Beats Music, the brainchild of music industry visionaries Jimmy Iovine and Trent Reznor.

We know music - we obsess over it, and devote our lives to it. We understand music is an experience, not a utility. We realize the heart and inspiration it takes to craft music and cherish the connection between the artist and the listener.

Musical taste is complex, evolving, and unique. We believe that hearing the right music at the right time enriches your life. It's why we're here: To deliver musical bliss, and move culture.

You

You’re friendly, positive, professional, “real”, and fun to work with!

You’re a creative, outside­the­box thinker with excellent problem­solving and decision­making ability. You’re proactive, self­starting, organized, and willing to take on tasks beyond job scope.

You have excellent communication skills, both written and verbal. You’re self­motivated, energetic, and passionate about music.
Responsibilities
Responsibilities:
Work with other Data Scientists to integrate and distill data from various sources for use in powering data-­informed features
Develop transparent and queryable Probabilistic Graphical Models (PGMs) to aid in developing personalized recommendations
Develop statistical models of personal music tastes based on a variety of music, behavioral, and contextual data points.
Experience
Skills & Experience:
Prior experience developing real­time, adaptive, probabilistic Machine Learning models
Experience with the following Machine Learning algorithms, methods, and techniques:
Bayesian Inference (MAP, MLE, EM, MCMC, etc.)
Matrix factorization and feature extraction (NMF, LDA, LSH, etc.)
Transparent, generative Probabilistic Graphical Modeling (PGM)
Random forest and other decision-­tree algorithms
Tensor Decomposition
Probabilistic Latent Semantic Analysis (PLSA)
Locally Sensitive Hashing (LSH)
Latent Dirichlet Allocation (LDA)
Topic Modeling
Social graphs, graph theory, graph theoretic inference
Familiarity with MATLAB, Mathematica, R, Juli or another scientific computing language
Experience working with Mahout, Weka, scikit­learn, mlpy, MALLET, GSL, or other third-­party machine learning tools and platforms
Proficient in Python or another expressive, high-level language.
Skills
Bonus Points:
Familiarity with NoSQL technologies like MongoDB, Redis, Riak, and Neo4j
Exposure to Storm, Hadoop, HBase, HDFS, Hive, Pig, Cascading, and other Big Data technologies
Previous experience working with music data, Music Information Retrieval, or Digital Signal Processing.
Education
Bachelor's Degree, Master's Degree
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