Sr. Research Scientist, AWS
AWS Talent Management Senior Research ScientistThe AWS T&D Research Science team is seeking an agile senior research scientist to lead building a science based Talent Management measurement strategy, evaluate the business impact of key talent and learning programs, and design new experimentation efforts accelerating our AWS employees' career trajectory and engagement.
In this role, you will be single threaded on designing an annual measurement strategy to evaluate, diagnose, and surface key insights that inform how our ongoing, talent management cycles impact critical outcomes such as:
AWS employee sentiment (i.
e.
intentions to stay, career satisfaction, etc.
), motivation and readiness for career growth, and longer-term outcomes such as retention, bench strength, and promotions velocity.
You will deep dive AWS survey response data, passive HR attribute data, and insights from ongoing research efforts to determine the current health of the talent management cycles, determine new research/pilots that should be established to address key gaps, develop hypotheses that can be tested, and use quantitative and qualitative analysis to deliver actionable insights to business and HR leaders.
You will use key insights generated from ongoing analysis and experimentation to build proactive reporting mechanisms that HRBPs and managers can use to quickly understand key trends, understand key success measures for talent management events, and monitor trending changes that require deeper levels of inspection/research.
This role will also be responsible for influencing future AWS/Amazon wide talent management tools, employee and manager resources, and ongoing measurement strategies that can be used to predict future success.
This role provides an opportunity for significant visibility and impact to the AWS business by surfacing critical people related insights through iterative experimentation, deep analysis, and close partnerships across the AWS flywheel.
If you have a desire to innovate and re-engineer new talent management mechanisms, are deeply technical as a scientist, and are seeking a long-term opportunity to build new solutions to challenging and ambiguous problems, Come Build With Us!The ideal candidate will demonstrate the following:
People Analytics and Interpretation.
Experience in all aspects of quantitative and qualitative research, people analytics/statistics, using R/SPSS/ or SAS to run various sophisticated predictive and causal analyses, and strong data interpretation and narrative writing skills.
Research Design.
Knowledge and deep experience in quasi experimental design, hypothesis testing, and controlling for threats to validity in an applied, fast paced environment.
Survey Design and Deployment.
Experience designing various business facing surveys (small and large scale), in addition leveraging multiple survey sampling approaches to target key populations and ensure insights are representative of a larger AWS population.
Deep Ownership.
Ability and willingness to conduct all research, analytics, and reporting activities, throughout the research pipeline (e.
g.
, identifying data sources, attaining data via SQL, shaping and analyzing the data, and writing up and communicating the results), as there is little opportunity in this role to outsource work to others.
Consulting Skills.
Utilize past experience, deep knowledge of processes and policies, and knowing what's going on across our organization to create custom science based solutions and localize as needed.
Strong Verbal and Written Communication.
Share your ideas, listen to others, follow-up, and follow-up again.
Use the language of the business and avoid consultant speak.
Can convey complex findings to a variety of audiences.
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 Talent Management POCs, HR Business Partners, Business Leaders, and corporate COE teams.
Share best practices, partner on solutions, and move the organizations forward together to adopt enhanced talent management solutions.
Systems Thinking.
Understands all the connections and integrations points through the entire talent management lifecycle, and builds those considerations into the research, experimentation, and final reporting to customers.
High Tolerance for Ambiguity.
Comfortable operating in a space with little direction while solving for complex problems with many types of stakeholders.
KEY
Responsibilities:
Build a Voice of the Customer (VOC) pulse monitoring mechanism that aids AWS TM and line-PXT to evaluate potential risks, hotspots, and success stories of talent management cycles (starting with the Q1 experience), in addition to identifying where the AWS HR team needs to redesign or alter future rollouts, tooling, and recommendations.
Apply varying survey sampling approaches to ensure representative sampling across multiple audiences (i.
e.
employees, front line managers, leaders, and HRBPs).
Manage full life cycle of large-scale talent management research programs (i.
e.
, develop measurement strategy, gather requirements, manage, and execute).
Lead end to end participant selection and measurement of critical AWS Learning Programs in order to ensure these programs are driving impact for our AWS business.
Conduct analysis, ongoing experimentation, and predictive modeling for a deep understanding of key insights that help evaluate the ongoing program health of the core talent management experience.
Partner closely with our Analytics, Employee Survey (Connections), Global Talent Management (GTM) 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 discussions.
Measure and assess the impact of new talent management and evaluation based resources introduced to the organization.
Continually evaluate solutions for quality, business impact, scalability and sustainability.
Conduct pre- and 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.
Basic
Qualifications:
Masters degree in a discipline of science, like industrial organizational psychology, applied statistics, or behavioral science.
5
years experience with quantitative statistics, including regressions, analysis of variance, and causal analysis.
5
years of survey research design, item analyses, and item statistics 5
years experience with the application of research and analytics to human capital, talent management, and Human Resources.
Proficiency in at least one statistical program (R, SAS, SPSS, etc.
) Proven written and verbal communication skills to convey complex subject matter to key stakeholders (writing sample may be requested) Ability to work independently and as part of a diverse team.
Experience showing thinking big and bias for action; comfortable navigating conflicting priorities and ambiguous problems.
Preferred
Qualifications:
Ph.
D.
in a discipline of science, like industrial organizational psychology, applied statistics, or behavioral science.
Experience using SQL and extracting data without relying upon others.
R and R shiny programming skills.
Using R to efficiently run various sophisticated statistical analyses and build experimental web based application used shiny.
Experience building, training and deploying machine learning models.
.
Estimated Salary: $20 to $28 per hour based on qualifications.
In this role, you will be single threaded on designing an annual measurement strategy to evaluate, diagnose, and surface key insights that inform how our ongoing, talent management cycles impact critical outcomes such as:
AWS employee sentiment (i.
e.
intentions to stay, career satisfaction, etc.
), motivation and readiness for career growth, and longer-term outcomes such as retention, bench strength, and promotions velocity.
You will deep dive AWS survey response data, passive HR attribute data, and insights from ongoing research efforts to determine the current health of the talent management cycles, determine new research/pilots that should be established to address key gaps, develop hypotheses that can be tested, and use quantitative and qualitative analysis to deliver actionable insights to business and HR leaders.
You will use key insights generated from ongoing analysis and experimentation to build proactive reporting mechanisms that HRBPs and managers can use to quickly understand key trends, understand key success measures for talent management events, and monitor trending changes that require deeper levels of inspection/research.
This role will also be responsible for influencing future AWS/Amazon wide talent management tools, employee and manager resources, and ongoing measurement strategies that can be used to predict future success.
This role provides an opportunity for significant visibility and impact to the AWS business by surfacing critical people related insights through iterative experimentation, deep analysis, and close partnerships across the AWS flywheel.
If you have a desire to innovate and re-engineer new talent management mechanisms, are deeply technical as a scientist, and are seeking a long-term opportunity to build new solutions to challenging and ambiguous problems, Come Build With Us!The ideal candidate will demonstrate the following:
People Analytics and Interpretation.
Experience in all aspects of quantitative and qualitative research, people analytics/statistics, using R/SPSS/ or SAS to run various sophisticated predictive and causal analyses, and strong data interpretation and narrative writing skills.
Research Design.
Knowledge and deep experience in quasi experimental design, hypothesis testing, and controlling for threats to validity in an applied, fast paced environment.
Survey Design and Deployment.
Experience designing various business facing surveys (small and large scale), in addition leveraging multiple survey sampling approaches to target key populations and ensure insights are representative of a larger AWS population.
Deep Ownership.
Ability and willingness to conduct all research, analytics, and reporting activities, throughout the research pipeline (e.
g.
, identifying data sources, attaining data via SQL, shaping and analyzing the data, and writing up and communicating the results), as there is little opportunity in this role to outsource work to others.
Consulting Skills.
Utilize past experience, deep knowledge of processes and policies, and knowing what's going on across our organization to create custom science based solutions and localize as needed.
Strong Verbal and Written Communication.
Share your ideas, listen to others, follow-up, and follow-up again.
Use the language of the business and avoid consultant speak.
Can convey complex findings to a variety of audiences.
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 Talent Management POCs, HR Business Partners, Business Leaders, and corporate COE teams.
Share best practices, partner on solutions, and move the organizations forward together to adopt enhanced talent management solutions.
Systems Thinking.
Understands all the connections and integrations points through the entire talent management lifecycle, and builds those considerations into the research, experimentation, and final reporting to customers.
High Tolerance for Ambiguity.
Comfortable operating in a space with little direction while solving for complex problems with many types of stakeholders.
KEY
Responsibilities:
Build a Voice of the Customer (VOC) pulse monitoring mechanism that aids AWS TM and line-PXT to evaluate potential risks, hotspots, and success stories of talent management cycles (starting with the Q1 experience), in addition to identifying where the AWS HR team needs to redesign or alter future rollouts, tooling, and recommendations.
Apply varying survey sampling approaches to ensure representative sampling across multiple audiences (i.
e.
employees, front line managers, leaders, and HRBPs).
Manage full life cycle of large-scale talent management research programs (i.
e.
, develop measurement strategy, gather requirements, manage, and execute).
Lead end to end participant selection and measurement of critical AWS Learning Programs in order to ensure these programs are driving impact for our AWS business.
Conduct analysis, ongoing experimentation, and predictive modeling for a deep understanding of key insights that help evaluate the ongoing program health of the core talent management experience.
Partner closely with our Analytics, Employee Survey (Connections), Global Talent Management (GTM) 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 discussions.
Measure and assess the impact of new talent management and evaluation based resources introduced to the organization.
Continually evaluate solutions for quality, business impact, scalability and sustainability.
Conduct pre- and 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.
Basic
Qualifications:
Masters degree in a discipline of science, like industrial organizational psychology, applied statistics, or behavioral science.
5
years experience with quantitative statistics, including regressions, analysis of variance, and causal analysis.
5
years of survey research design, item analyses, and item statistics 5
years experience with the application of research and analytics to human capital, talent management, and Human Resources.
Proficiency in at least one statistical program (R, SAS, SPSS, etc.
) Proven written and verbal communication skills to convey complex subject matter to key stakeholders (writing sample may be requested) Ability to work independently and as part of a diverse team.
Experience showing thinking big and bias for action; comfortable navigating conflicting priorities and ambiguous problems.
Preferred
Qualifications:
Ph.
D.
in a discipline of science, like industrial organizational psychology, applied statistics, or behavioral science.
Experience using SQL and extracting data without relying upon others.
R and R shiny programming skills.
Using R to efficiently run various sophisticated statistical analyses and build experimental web based application used shiny.
Experience building, training and deploying machine learning models.
.
Estimated Salary: $20 to $28 per hour based on qualifications.
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