What You'll Need :
- 2+ years hands-on experience building statistical models, machine learning techniques, recommendation systems and shallow and deep learning techniques.
- Experience and proficiency with various programming languages (e.g., Python), machine learning tools (e.g., scikit-learn), statistical packages (e.g., Scipy), SQL/relational databases (e.g., Oracle) and NoSQL databass (e.g., MongoDB, graph database), Linux and shell scripting.
- Familiar with the foundational approaches to the major data science disciplines, such as data preparation, advanced statistics, machine learning, simulation, and natural language processing
- Experience and proficiency with SQL queries, Hadoop, Spark, Elasticsearch & Python.
Desirable :
- Good understanding of advanced statistical techniques including clustering, classification, regression, matrix factorization, recommendation, feature selection etc.
- Basic understanding of advanced machine learning models including deep learning models such as CNN, RNN, LSTM etc.
- Past experience on working in Apache Solr, NLP and/or computer Vision Projects.
- Bachelors/Masters in a quantitative research field (e.g., Computer Science, Electrical Engineering, Applied Mathematics, Physics, etc.). PhD preferred.
- Experience of running large-scale backend services that host complex models and algorithms.
- Hands on experience in working with AWS or other cloud platform.
- Experience on working with structured and unstructured data on data aggregation, sub-setting, manipulation and pre-processing.
- Experience of creating and deploying machine learning models in cloud platform.
- Practical, intuitive problem solver with a demonstrated ability to translate business objectives into actionable data science tasks and translate quantitative analysis into actionable business strategies
- Connections to the recommendations and / or information retrieval academic community
- Ability to work in a culture that thrives on feedback and seeks opportunities to stretch outside comfort zone.
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