expectations Sales acumen, identifying and managing sales opportunities at client engagements An understanding of database technologies e.g. SQL, ETL, No-SQL, DW, and Big Data technologies e.g. Hadoop, Mahout, Pig, Hive, etc.; An understanding of statistical modelling techniques e.g. Classification and regression techniques, Neural Networks, Markov chains, etc.; An understanding of cloud technologies e.g. AWS, GCP or Azure A More ❯
expectations Sales acumen, identifying and managing sales opportunities at client engagements An understanding of database technologies e.g. SQL, ETL, No-SQL, DW, and Big Data technologies e.g. Hadoop, Mahout, Pig, Hive, etc.; An understanding of statistical modelling techniques e.g. Classification and regression techniques, Neural Networks, Markov chains, etc.; An understanding of cloud technologies e.g. AWS, GCP or Azure A More ❯
expectations Sales acumen, identifying and managing sales opportunities at client engagements An understanding of database technologies e.g. SQL, ETL, No-SQL, DW, and Big Data technologies e.g. Hadoop, Mahout, Pig, Hive, etc.; An understanding of statistical modelling techniques e.g. Classification and regression techniques, Neural Networks, Markov chains, etc.; An understanding of cloud technologies e.g. AWS, GCP or Azure A More ❯
mathematical software (e.g. R, SAS, or Matlab) - Experience with statistical models e.g. multinomial logistic regression - Experience in data applications using large scale distributed systems (e.g., EMR, Spark, Elasticsearch, Hadoop, Pig, and Hive) - Experience working with data engineers and business intelligence engineers collaboratively - Demonstrated expertise in a wide range of ML techniques PREFERRED QUALIFICATIONS - Experience as a leader and mentor More ❯
Statistics, Applied Mathematics, or Engineering - Strong experience with Python and R - A strong understanding of a number of the tools across the Hadoop ecosystem such as Spark, Hive, Impala & Pig - An expertise in at least one specific data science area such as text mining, recommender systems, pattern recognition or regression models - Previous experience in leading a team, ideally of More ❯
drive marketing efficiencies by leveraging approaches that optimize Amazon's systems using cutting edge quantitative techniques. The right candidate needs to be fluid in: Data warehousing and EMR (Hive, Pig, R, Python). Feature extraction, feature engineering and feature selection. Machine learning, causal inference, statistical algorithms and recommenders. Model evaluation, validation and deployment. Experimental design and testing. BASIC QUALIFICATIONS More ❯
