Senior Data Scientist We are looking for a passionate, creative Senior Data Scientist to join Inkiru Data Science team at Client Global Tech. You will have the unique opportunity to work with massive business and social data generated by Client Inc.'s unparalleled online and offline commerce. You will be responsible to use cutting-edge machine learning, data mining, and optimization techniques to design and prototype robust, scalable predictive models and analytics solutions. You will be a part of a cross-functional team of Data Scientists, Engineers, Product managers, and Business professionals that sit at the intersection of Data Science, Big data, IT infrastructure, and business operations. Be a strategic partner to product development teams, sharing insights and providing recommendations through effective communication and storytelling with data. Design, build and implement predictive models with high-dimensional, fast-moving real-time datasets. Use machine learning, deep learning and data mining techniques to develop robust predictive models for anomaly detection such as E-Commerce payment risk, membership abuse risk and identity risk assessment. Design, analyze, and run both simulated and live experiments (A/B and multivariate tests) to drive KPI improvements. Build data visualizations and interactive dashboards for monitoring and sharing business insights internally and externally. Interested in researching about new methods, tools, and techniques to address complex business problems. Technical expertise in data mining, machine learning, and statistical analysis. Proficient in machine learning and data mining packages in Python. Programming skills in one of the following languages: Java, or JavaScript on a UNIX or Linux platform. Experience working with large data sets and distributed computing tools (BigQuery and GCP experiences preferred). Advanced degree (Master or Ph.D.) in any STEM field plus three years of related experience. Experiences building and deploying deep learning models in production. Experiences in the fraud detection and abuse detection domain.
