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- Deep understanding of statistical modeling, machine learning, big data analytics, and algorithmic analysis. Ability to select approaches and algorithms to problems and articulate why those specific solutions are appropriate. This requires some experience and theoretical knowledge.
- Comprehension of algorithms in the following areas: Bayesian Data Analysis, Classification, Clustering, Regression, Collaborative Filtering, and Graph Algorithms.
- Knowledge of data structures, data modeling, replication and distribution, data/object relational database mapping.
- Experience working with the Hadoop ecosystem, including Hive, Spark, and MapReduce, big data workflow management tools such as Airflow, and development and use of custom UDFs (User-Defined Functions) in Hive.
- Experience with SQL, Python, and Java.
- Design, develop, test, and refine the statistical algorithms used to power the world’s leading Data Management Platform, the Lotame DMP;
- Apply experience in data modeling, data mining and optimization to very large scale data sets, producing solutions to meet Lotame’s product suite;
- Work directly with Lotame’s Data Science team to provide statistical algorithm development and support to a variety of products, including Lotame’s Optimizer and Cross Device;
- Independently execute data science projects, working with product, engineering and/or customer service teams to identify requirements, perform analysis, and present results and/or solutions to stakeholders;
- Develop products in the areas of performance optimization, behavioral profiling, audience segmentation and similar use cases;
- Investigate, propose, and implement new technologies that may enhance Lotame’s data capabilities and efficiency.
- Deep understanding of statistical modeling, machine learning, big data analytics, and algorithmic analysis. Ability to select approaches and algorithms to problems and articulate why those specific solutions are appropriate. This requires some experience and theoretical knowledge.
- Comprehension of algorithms in the following areas: Bayesian Data Analysis, Classification, Clustering, Regression, Collaborative Filtering, and Graph Algorithms.
- Knowledge of data structures, data modeling, replication and distribution, data/object relational database mapping.
- Experience working with the Hadoop ecosystem, including Hive, Spark, and MapReduce, big data workflow management tools such as Airflow, and development and use of custom UDFs (User-Defined Functions) in Hive.
- Experience with SQL, Python, and Java.
- Understanding of and experience with basic web technologies such as URLs, http(s), html, TCP/IP, JSON, web-based APIs.
- Experience working with datasets in a cloud (AWS, Azure, Google) environment, including tools such as EMR, S3, and Athena, while not required, will be beneficial.
- Bachelor’s Degree in Statistics, Mathematics, Engineering or a closely related field.
- Master’s Degree preferred