The Alexa Feedback organization owns a number of programs and domains which drive high customer engagement and feature discovery, maintains customer trust, understands customers’ feedback, develops new features that provide utility value for customers with special needs, and develops locally relevant experiences. The Alexa Experience Science team applies machine learning and natural language understanding algorithms to improve these programs and the functionality of domains such as News (“Alexa, What's the news?"), Feedback (“Alexa, that was wrong!"), Personality (“Alexa, what’s your favorite color?”), and to advance Alexa’s ability to handle more ambiguous utterances.
We’re looking for a passionate, talented, and inventive Scientist to help build industry-leading ML technologies that help provide the best-possible experience for our customers. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to develop new features, predict key user behaviors and deliver automated decisions, both offline and in real time.
What You Will Do
· Step in as an experienced member of a machine learning research team, establish technical credibility quickly, and help recruit elite machine learning practitioners.
· Contribute to setting up the research vision for the team, and develop and execute on a roadmap that addresses the major questions faced by the domains we serve.
· Raise the bar for research quality and impact. Stay up to date on research results both within and complementary to the Alexa technology space and helping both your team and the larger org utilize tried patterns and state of the art results to work smarter and faster.
· See it through — don’t stop at delivering a report or a model, use your work to influence and create durable change in how leaders at Alexa think about problems.
· Solve problems durably — design processes, tools, and programs that solve entire categories of problems without you or your team’s direct intervention.
· Hands-on practitioner - You yourself are actively involved in performing data analysis, A/B experimentation and modeling with large data sets to develop insights that increase device usage and customer experience
· Work closely with product managers and software engineers to design experiments and implement end-to-end solutions. Communicate the results of your optimizations to various business stakeholders
· Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
· Develop the skills of junior members through mentorship and training.
· PhD in highly quantitative field (CS, machine learning, mathematics, statistics, physics) or equivalent experience.
· 5+ years of experience with machine learning, statistical modeling, data mining, and analytics techniques.
· Previous experience in a ML or data scientist role with a large technology company.
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
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
· Bachelor or Master's degree in highly quantitative field (CS, machine learning, mathematics, statistics, physics) or equivalent experience.
· Experience with R, Python, SAS, Matlab or other statistical/machine learning software.
· Experience applying various machine learning techniques, and understanding the key parameters that affect their performance.
· Experience developing experimental and analytic plans for data modeling processes, use of strong baselines, and the ability to accurately determine cause and effect relationships.
· Have a history of building systems that capture and utilize large data sets in order to quantify performance via metrics or KPIs.
· Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.