The Alexa Voice Services (AVS) & Skills Science team uses data to inform the most critical strategic business decisions that impact how customers engage with Alexa using skills (i.e., voice apps) and third-party hardware (e.g., Sonos, Bose, FitBit). As a Research Scientist output from your research will directly influence the marketing and product teams that help customers discover and engage with Alexa in more ways on more devices.
The ideal candidate will have an extensive background extracting insights from complex data sets. They will also be a strong communicator, able to describe scientifically rigorous work to business stakeholders of varying levels of technical sophistication. They will be a partner to our business stakeholders, going beyond answering the question asked by pushing the business to consider new ways of examining its performance.
· Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences
· Extensive experience applying theoretical models in an applied environment
· Previous experience in a ML or data scientist role with a large technology company
· Experience in developing machine-learning algorithms, statistical and mathematical optimization models, and simulation and visualization tools
· Personal interest in artificial intelligence, voice assistants, or natural language understanding
Ideal candidate profile
· PhD or equivalent Master's degree plus 4+ years of research experience in a quantitative filed
· Experience investigating the feasibility of applying scientific principals and concepts to business problems and products
· Experience with R, Python, or other statistical/machine learning software
· Understanding of regression modeling, forecasting techniques, time series analysis, machine-learning concepts such as supervised and unsupervised learning, classification, random forest, etc.
· Experience developing experimental and analytic plans for data modeling processes and baselines
· Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
· Knowledge of SQL