Data Scientist InternBookmark This
2601 Ocean Park Blvd
Santa Monica, CA
DescriptionBased in sunny Santa Monica, Retention Science is the leader in Retention Marketing. We are building the first “Retention Automation Platform” – a B2B enterprise software designed to help e-Commerce increase existing customer spending. Through cutting-edge predictive technology and big data analytics, we help e-Commerce businesses understand customer behaviors and maximize customer retention. Our software-as-a-service profiling engine uses machine learning techniques and statistical models to analyze purchasing trends based on massive demographics, social, and behavior data sets. We use these data points to automatically generate retention strategies for each individual customer.
Our Retention Science team consists of serial entrepreneurs from Caltech, UC Berkeley, Stanford and Yale. Our founders have been recognized as the Ernst & Young Entrepreneur of the Year. Additionally, we have been named Top 10 Big Data Startup of the Year in 2012 by CRN, “Innovation Agent” in 2013 by Fast Company and Top 10 Software Company in Southern California in 2013 by SocalTech. Retention Science was announced on Fox News LA last year as one of the most promising startups to watch. We have also been featured in Forbes, Inc Magazine, TechCrunch, Bloomberg, and Reuters, to name a few.
We are a close-knit family who drink a little too much coffee, work late nights and brainstorm creative new ways to improve our solutions. Join us if you are interested in working in a dynamic and exciting start-up environment, being part of a world-class team, receiving individual and company performance bonuses, and indulging in free snacks, drinks, and food!
We are looking for a Data Scientist Intern with experience in machine learning, distributed computing to work with our expert engineering team to architect our big data infrastructure. You will have a great learning opportunity in data architectural approach and execution. We are looking for a hands-on coder who enjoys implementing algorithms as much as designing and fine-tuning them.
Responsibilities-Design scalable predictive models with large number of features
-Hold a stake in the entire machine learning implementation process: model design, feature planning, system infrastructure, production setup and monitoring, and release management
-Use machine learning techniques optimized for distributed computing environments
Requirements-Basic knowledge of predictive analytics/statistical modeling/machine learning such as regression analysis, predictive models, Bayesian classification, collaborative filtering, decision trees, clustering problems applied to large data sets
-Expert in at least one of: Ruby, Python, Java, C++
-Good understanding of web technologies and Unix/Linux
-Some familiarity with distributed systems and methodologies: Hadoop, MapReduce, Cascading, Hive, Pig
-Some experience with NoSQL / graph databases: Neo4J, MongoDB, Riak, Cassandra
-Some experience with cloud technologies: AWS, Rackspace