Perform hands-on data analysis and modeling with huge data sets. Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms . Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products . Discover data sources, get access to them, import them, clean them up, and make them model-ready.
You need to be willing and able to do your own ETL. Create and refine features from the underlying data. Youll enjoy developing just enough subject matter expertise to have an intuition about what features might make your model perform better, and then youll lather, rinse and repeat.
Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your optimizations and communicate results to peers and leaders . Explore new design or technology shifts in order to determine how they might connect with the customer benefits we wish to deliver. Qualifications BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.). 2+ years experience with data science. Experience in modern advanced analytical tools and programming languages such as R or Python with scikit-learn.
Effecient in SQL, Hive, or SparkSQL, etc. Comfortable in Linux environment Experience in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, anomaly detection, recommender systems, sequential pattern discovery, and text mining. Solid communication skills Demonstrated ability to explain complex technical issues to both technical and non-technical audiences. Preferred Additional Experience Apache Spark The Hadoop ecosystem Java HP Vertica TensorFlow, reinforcement learning Ensemble Methods, Deep Learning, and other topics in the Machine Learning community