Sr. Research Scientist, AWS Talent and Development

Job ID: 1319175 | Amazon Web Services, Inc.

DESCRIPTION


Are you passionate about conducting research to drive real behavioral change and inform front line managers and leaders in making more effective decisions? Would you love to see your research in practice, impacting Amazonians globally and improving the employee experience? If so, you should consider joining the Amazon Web Services (AWS) Organizational Effectiveness and Science team.

AWS is a dynamic, growing business unit within Amazon.com. Since early 2006, AWS has provided companies of all sizes with an infrastructure platform in the cloud. Today, AWS provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers millions of active monthly customers from start-ups to multi-national corporations to governments.

In this role, you will lead the research strategy to evaluate, diagnose, understand, and surface drivers and moderators for key, global research streams. These include (but are not limited to) productivity, innovation, decision-making, and Amazon culture. You will dive deep to understand what research should be conducted, develop hypotheses that can be tested, and use both quantitative and qualitative research to deliver actionable insights to AWS leaders. For example, a recent research project has looked at how Amazon’s organizational culture spreads throughout the organization by looking at employee-, team-, department-, and country-level characteristics.

The ideal candidate must be self-driven, possess a high degree of ownership, have strong business acumen, excellent communication skills, planning/project management skills, and thrive working in a fast-paced environment. This role requires dealing with a high level of ambiguity and feeling comfortable operating in a space with little direction for solving hard, complex problems. Candidates should be comfortable with nested modeling to assess the impact that variables at the team- and organizational-level has on individual-level outcomes. Experience with applying machine learning to develop classification models as well as forecasting models is a plus, but not essential. There is no opportunity in this role to outsource work to others, so candidates must be comfortable taking on a several activities (i.e., identifying data sources, attaining data via SQL, shaping and analyzing the data, and writing up and communicating the results).


The successful candidate will also have a demonstrated track record of:
· Knowing Your Customers. Understand their priorities, what they are saying, and more importantly, what they aren’t. Deliver and drive value for them.
· Innovation. Appreciate what exists, identify why it works. Then rebuild it or build something totally different that derives even MORE value and a better customer experience.
· Judgment. Take different perspectives and business needs, develop a solution that works, move the ball forward. Be able to support your opinions with sound reasoning grounded in the business.
· Systems Thinker. Understand all the connections and integrations points through the entire talent management lifecycle.
· Thinking big. How do we build it bigger, better, faster? What aren’t we thinking of?
· Research driven. We seek to be the most scientific HR organization in the world. We form hypotheses about the best talent management techniques and then set out to prove or disprove them with experiments and careful data collection.
· Prioritization. There will be a constant flow of work, both tactical and strategic. Determine what gets done first and why, while managing a plan for what to do with everything else.
· Building relationships. Partner with AWS HR COE teams, HR Business Partners, Business Leaders, and corporate COE teams. Share best practices, partner on solutions, and move the organizations forward together.
· Consulting. Utilize past experience, deep knowledge of processes and policies, and knowing what’s going on across our organization to create custom solutions and localize as needed.
· Communicating. Share your ideas, listen to others, follow-up, and follow-up again. Use the language of the business and avoid “consultant speak”.
· Resourcefulness. If you don’t know it, that’s ok. But you should know where to go for the answer or how to find out.
· Bias for Action and dealing with ambiguity. Sometimes it is not clear how we are going to get there. Can you help carve a path? How fast can you do it? What are the tradeoffs? Take risks and be willing to try new things, fail fast and iterate.
KEY RESPONSIBILITIES:
· Manage full life cycle of large-scale research programs (i.e., develop strategy, gather requirements, manage, and execute).
· Conduct experiments, analytics, and compute behavioral process models for a deep dive understanding of systemic and individual level drivers; combine quantitative and qualitative data to inform research.
· Conduct organizational analysis to uncover talent and org effectiveness insights and build talent and organizational effectiveness solutions (both scrappy or long-term) and mechanisms with the objective to help the business increase talent leverage and improve organizational speed and execution.
· Partner closely with our Analytics, Employee Survey (“Connections”), and Diversity and Inclusion teams to identify opportunities for integration points and address specific talent needs leveraging talent data and themes.
· Partner with AWS Analytics team to automate organizational health reporting and innovative tools to uncover organizational insights to drive strategic talent and organizational discussions.
· Measure and assess the impact of tools and resources introduced to the organization. Continually evaluate solutions for quality, business impact, scalability and sustainability. Conduct post launch evaluations to understand successes and improvement opportunities while establishing methods for sustained adoption.
· Evaluate research initiatives to provide bottom line value, ROI, and incremental improvements over time
If you have an entrepreneurial spirit, know how to deliver, are deeply technical, highly innovative and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you.

BASIC QUALIFICATIONS

· Masters degree in Industrial/Organizational or Applied Psychology
· Experience conducting multi-variate analysis and research experiments applying the findings to make business impact
· Six or more years of applying statistical algorithms and models to solving applied problems in industry
· Ability to pull and merge large data sets from various data warehouses


PREFERRED QUALIFICATIONS

· Foundational skills in conducting experimental research studies and data analysis
· Proficiency in at least one statistical program (R, SAS, SPSS, etc.
· Outstanding written and verbal communication skills
· Experience with guiding R&D strategy for your organization
· Ph.D. in applied psychology
· R and R-Shiny programing skills
· Experience using organizational network analysis software
· Proven record of innovation and strategic impact across teams
· Excellent interpersonal skills and a can-do never-give-up attitude
· Ability to work independently and as part of a diverse team
· Meets/exceeds Amazon’s leadership principles requirements for this role
· Meets/exceeds Amazon’s functional/technical depth and complexity for this role

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.