Machine Learning Engineers work to deploy end-to-end solutions to business problems leveraging AI and/or ML principles as needed to create those solutions. MLEs will take requests from stakeholders, define the components required for the project, gather data necessary for project EDA and training, then work with stakeholders to develop a plan around the productionized use of the solution, and work to put that solution into final production.

• Consult with stakeholders to gather business requirements, translate them into data solutions, design high-level model structures and demonstrate deep expertise in advanced analytics techniques (e.g., AI and ML) to design, prototype, and build solutions to business problems
• Lead communication with other stakeholders to drive use case development and manage expectations on model limitations and lead times
• Analyze data to identify useful relations, patterns and features that are predictive of user behaviors, preferences, intents, interests
• Manage and execute entire projects from start to finish, including cross-functional project management; data collection and manipulation, analysis and modeling; communication of insights and recommendations; productionalization of final model products
• Share findings with stakeholders to improve business decisions and/or influence strategic direction.
• Monitor and stay updated with industry trends and emerging technologies to identify opportunities for innovation and improvement
• Developing and maintaining the end to end modeling code and standardizing the code for reusability in the production environment.
• Profiling users including customer segmentation to help the marketing team to target specific audience for upgrading to services and also for the user retention

• Degree in a quantitative discipline, such as Data Science, Applied Mathematics, Statistics, Economics, Operations Research, Computer Science, Mathematics, Physics, Biology, Chemistry or Engineering. An advanced degree, Data Science bootcamp or MOOC certification is a plus.
• 3-5 years of work experience in classification, regression, clustering, natural language processing NLP, experiments, and optimization.
• Ability to apply Bayesian inference, frequentist statistics, causal modeling, and / or machine learning techniques.
• Experience with any of these: customer segmentation, campaign targeting and effectiveness, A/B experiments, quasi-experiments, sales forecasting, churn propensity modeling, customer lifetime value analysis, credit risk, geospatial analytics, survey key-drivers, marketing mix modeling, multi-touch attribution, or recommender systems.
• Highly skilled in R and Python for statistical and machine learning programming.
• Highly skilled in SQL & Python coding to wrangle and explore structured & unstructured data.
• Proficient with server or Cloud computing platforms, such as Google Compute Engine or EC2.
• Proficient with data warehouses, such as Oracle, Big Query, or AWS.
• Subject matter scientist that can review the literature to identify state of the art solutions to a business problem.