Role at a glance
- Job function
-
Marketing & Communications Marketing Strategy
- Salary
- Not Disclosed
- Location
- New York, New York, United States Boston, Massachusetts, United States
- Work arrangement
- On-site
- Education
- Bachelor's, Master's
Spotted an issue?
We’ll check it against the original posting.
Qualifications
Required
- 5+ years of Go-To-Market, Business Development, Sales, or Consulting experience
- 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- 5+ years of in program management, workforce strategy development, or metrics-driven decision making experience
- Bachelor's degree
- Experience selling enterprise software or cloud-based applications
- Experience explaining complex technical concepts to various business and technical audiences
- Experience presenting to both technical and non-technical executive audiences
- Experience leading complex, multi-year initiatives that may be cross-functional and/or span business and technology
Preferred
- Master's degree in business, data science, public administration, finance, engineering, human resources, or related field, or PMP certificate
- Experience interpreting data and making business recommendations
- Experience identifying, negotiating, and executing complex legal agreements
- Experience interpreting data and making business recommendations across leadership and cross-functional teams
- Experience using analytical tools for workforce metrics and reporting
About the role
Original posting provided by amazon
Are you excited about turning signal into action at scale? We are looking for a Senior Data & AI GTM Specialist to build and own the campaign execution arm that activates the AWS field the moment we detect a meaningful signal. Today our team synthesizes field, customer, and product signals into structured go-to-market recommendations. This role extends that capability by owning what happens next, converting a detected signal into a targeted, sequenced campaign that reaches the right field teams and the right customers within minutes to days rather than weeks or months.
Signals come from everywhere. Internally, they surface through seller conversations, deal blockers, product feature requests, , opportunity and pipeline data, and win and loss patterns. Externally, they surface through competitive moves, model launches, customer forum activity, and industry trends. Your job is to make sure that when a signal crosses a threshold worth acting on, a well-designed campaign fires automatically or near-automatically, with differentiated asks grounded in each account's context.
This is not traditional business development where you manage a handful of named accounts. Instead, you will design the mechanism that listens for signal, decides which signals warrant field activation, and orchestrates the outreach, enablement, and follow-through that turns that activation into pipeline. You will build this on top of our existing signal synthesis program and increasingly leverage agentic AI to move from manual triage toward an always-on detect-and-activate loop. You will own the full path from signal to seller action to measurable pipeline outcome.
You will be a deep expert in using AI to derive signal from noise and in using AI to execute on that signal at scale. This is the defining technical skill of the role. You will apply AI both to mine thousands of internal and external inputs for the patterns that matter and to turn those patterns into field activation faster and more precisely than any manual process could.
Key job responsibilities
Own the signal-to-activation mechanism end to end, defining which internal and external signals warrant field activations, campaigns, asset updates, or knowledge base improvements, and the closed-loop tracking that confirms the field acted and the signal was resolved
Design and launch targeted activation campaigns that convert a detected signal into a differentiated, sequenced field ask, reaching the right sellers and customers with context-aware messaging rather than undifferentiated broadcast
Build on the existing GTM signal synthesis capability, extending it from insight generation into campaign execution so that structured recommendations translate directly into field motion
Establish differentiated, cohort-based targeting that segments accounts by journey stage, existing pipeline, and prior actions, ensuring each seller receives an ask proportional to where their account already is
Ingest and normalize signal from diverse sources, including seller conversations, internal AI assistant prompts, deal blockers, product feature requests, opportunity data, competitive intelligence, model launches, and customer forums
Be a deep expert in using AI to derive signal and to execute on signal at scale, applying AI both to mine thousands of internal and external inputs for the patterns that matter and to convert those patterns into precise field activation faster than any manual process
Operate agentic AI mechanisms that mine signal in real time, prioritize what matters, and increasingly auto-generate the activation and recommendation, moving the team from manual triage toward an always-on loop
Drive internal teams to build and maintain the signal architecture, holding the source teams accountable for the pipelines, instrumentation, and data quality that keep signal flowing reliably from every internal and external source
Partner with Finance and Sales Operations to surface the top signals that are stalling deals in business reviews, quantifying blocked pipeline by root cause and turning that view into prioritized field action
Partner cross-functionally with the signal synthesis owner, product and service GTM teams, demand marketing, field enablement, sales leadership, and analytics to align activation campaigns to product priorities and field capacity
Define the business requirements for reporting that quantifies activation impact and blocked pipeline by root cause, giving leadership visibility into what signals drove which outcomes
Measure, report, and optimize activation campaigns against pipeline generated, conversion rate, deal velocity, signal resolution time, and revenue influence, presenting results to senior leadership
Continuously improve the mechanism, running experiments on signal thresholds, targeting logic, messaging, and channels so activation gets sharper and faster over time
A day in the life
You start your morning reviewing the overnight signal queue. Your AI mining mechanism has flagged a spike in field conversations and internal AI assistant prompts referencing a competitor's new product launch. You assess whether it clears the threshold for a field activation, confirm it does, and begin shaping the campaign. You define the target cohort using existing account intelligence, so that sellers already engaged on the topic get a lighter validate-and-advance ask while sellers with no engagement get the full play.
Mid-morning, you meet with Finance and Sales Operations to review the top deal blockers stalling pipeline this month. You work through which blockers trace back to product gaps, unlaunched models, or competitive pressure, quantify the blocked pipeline behind each, and decide which ones warrant a field activation or an escalation to the owning product team. You also spend time with the internal teams that own the upstream signal sources, holding them accountable for the instrumentation and data quality that keep signal flowing reliably.
After lunch, you work with the product GTM lead for a service approaching a major milestone. You align on positioning and which customer segments to prioritize, then wire the launch into your activation mechanism so that the moment the signal goes live, the field receives a coordinated ask rather than a scattered one. You also review how your agentic mining agent performed on last week's signals, tuning its prioritization so the highest-value patterns rise to the top automatically.
Later in the afternoon, you pull together a metrics read-out for leadership. You show pipeline influenced by activation, signal resolution time, and how differentiated targeting is outperforming broadcast on both engagement and deal size. You call out one activation exceeding targets and recommend scaling it, and you flag a signal source that is generating noise rather than value and propose refining its threshold.
Before wrapping up, you spend time in the field channels where sellers are responding to your latest activation. A few surface customer stories and blockers worth capturing. You feed those back into the mechanism to sharpen the next activation and to ensure each blocker gets a tracked response, closing the loop between what the field told us and what we did about it.
About the team
We are the Data & AI GTM Acceleration team within Amazon Web Services. Our mission is to help every AWS customer realize the value of AI and data services faster. We build and operate go-to-market programs and initiatives that connect the right customer with the right solution at the right moment, at a scale that a traditional sales model cannot reach on its own.
We sit at the center of a broad ecosystem. On one side, we work with AWS product and GTM teams building AI and data services. On the other, we partner with thousands of field sellers, solution architects, and inside sales professionals who bring those services to customers. Our job is to bridge the gap between what the field is telling us and what the field does next, turning signal into coordinated action.
What makes this team unique is how we operate. We build, we prove, and we scale. We prototype new go-to-market mechanisms, validate them with real sellers and real customers, and then hand proven playbooks to enterprise scaling partners who deploy them across the global AWS field. We move quickly, experiment often, and measure everything. We are increasingly AI-native in how we work, building and operating agents that mine field signal and deliver personalized recommendations directly into seller workflows.
Culturally, we are builders obsessed with measurable impact. We operate with a high degree of autonomy, trust each other to own outcomes end to end, and maintain a bias for action even when the path forward is not fully defined. You will work alongside people who are curious, technically fluent, collaborative across organizational boundaries, and energized by the pace of change in AI.
- 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- 5+ years of in program management, workforce strategy development, or metrics-driven decision making experience
- Bachelor's degree
- Experience selling enterprise software or cloud-based applications
- Experience explaining complex technical concepts to various business and technical audiences
- Experience presenting to both technical and non-technical executive audiences
- Experience leading complex, multi-year initiatives that may be cross-functional and/or span business and technology
- Experience interpreting data and making business recommendations
- Experience identifying, negotiating, and executing complex legal agreements
- Experience interpreting data and making business recommendations across leadership and cross-functional teams
- Experience using analytical tools for workforce metrics and reporting
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
USA, CA, Mountain View - 162,700.00 - 220,200.00 USD annually
USA, CA, San Diego - 147,900.00 - 200,100.00 USD annually
USA, CA, San Francisco - 162,700.00 - 220,200.00 USD annually
USA, GA, Atlanta - 147,900.00 - 200,100.00 USD annually
USA, MA, Boston - 147,900.00 - 200,100.00 USD annually
USA, NY, New York - 162,700.00 - 220,200.00 USD annually
USA, TX, Austin - 147,900.00 - 200,100.00 USD annually
USA, TX, Dallas - 147,900.00 - 200,100.00 USD annually
USA, VA, Arlington - 147,900.00 - 200,100.00 USD annually
USA, WA, Seattle - 147,900.00 - 200,100.00 USD annually
Signals come from everywhere. Internally, they surface through seller conversations, deal blockers, product feature requests, , opportunity and pipeline data, and win and loss patterns. Externally, they surface through competitive moves, model launches, customer forum activity, and industry trends. Your job is to make sure that when a signal crosses a threshold worth acting on, a well-designed campaign fires automatically or near-automatically, with differentiated asks grounded in each account's context.
This is not traditional business development where you manage a handful of named accounts. Instead, you will design the mechanism that listens for signal, decides which signals warrant field activation, and orchestrates the outreach, enablement, and follow-through that turns that activation into pipeline. You will build this on top of our existing signal synthesis program and increasingly leverage agentic AI to move from manual triage toward an always-on detect-and-activate loop. You will own the full path from signal to seller action to measurable pipeline outcome.
You will be a deep expert in using AI to derive signal from noise and in using AI to execute on that signal at scale. This is the defining technical skill of the role. You will apply AI both to mine thousands of internal and external inputs for the patterns that matter and to turn those patterns into field activation faster and more precisely than any manual process could.
Key job responsibilities
Own the signal-to-activation mechanism end to end, defining which internal and external signals warrant field activations, campaigns, asset updates, or knowledge base improvements, and the closed-loop tracking that confirms the field acted and the signal was resolved
Design and launch targeted activation campaigns that convert a detected signal into a differentiated, sequenced field ask, reaching the right sellers and customers with context-aware messaging rather than undifferentiated broadcast
Build on the existing GTM signal synthesis capability, extending it from insight generation into campaign execution so that structured recommendations translate directly into field motion
Establish differentiated, cohort-based targeting that segments accounts by journey stage, existing pipeline, and prior actions, ensuring each seller receives an ask proportional to where their account already is
Ingest and normalize signal from diverse sources, including seller conversations, internal AI assistant prompts, deal blockers, product feature requests, opportunity data, competitive intelligence, model launches, and customer forums
Be a deep expert in using AI to derive signal and to execute on signal at scale, applying AI both to mine thousands of internal and external inputs for the patterns that matter and to convert those patterns into precise field activation faster than any manual process
Operate agentic AI mechanisms that mine signal in real time, prioritize what matters, and increasingly auto-generate the activation and recommendation, moving the team from manual triage toward an always-on loop
Drive internal teams to build and maintain the signal architecture, holding the source teams accountable for the pipelines, instrumentation, and data quality that keep signal flowing reliably from every internal and external source
Partner with Finance and Sales Operations to surface the top signals that are stalling deals in business reviews, quantifying blocked pipeline by root cause and turning that view into prioritized field action
Partner cross-functionally with the signal synthesis owner, product and service GTM teams, demand marketing, field enablement, sales leadership, and analytics to align activation campaigns to product priorities and field capacity
Define the business requirements for reporting that quantifies activation impact and blocked pipeline by root cause, giving leadership visibility into what signals drove which outcomes
Measure, report, and optimize activation campaigns against pipeline generated, conversion rate, deal velocity, signal resolution time, and revenue influence, presenting results to senior leadership
Continuously improve the mechanism, running experiments on signal thresholds, targeting logic, messaging, and channels so activation gets sharper and faster over time
A day in the life
You start your morning reviewing the overnight signal queue. Your AI mining mechanism has flagged a spike in field conversations and internal AI assistant prompts referencing a competitor's new product launch. You assess whether it clears the threshold for a field activation, confirm it does, and begin shaping the campaign. You define the target cohort using existing account intelligence, so that sellers already engaged on the topic get a lighter validate-and-advance ask while sellers with no engagement get the full play.
Mid-morning, you meet with Finance and Sales Operations to review the top deal blockers stalling pipeline this month. You work through which blockers trace back to product gaps, unlaunched models, or competitive pressure, quantify the blocked pipeline behind each, and decide which ones warrant a field activation or an escalation to the owning product team. You also spend time with the internal teams that own the upstream signal sources, holding them accountable for the instrumentation and data quality that keep signal flowing reliably.
After lunch, you work with the product GTM lead for a service approaching a major milestone. You align on positioning and which customer segments to prioritize, then wire the launch into your activation mechanism so that the moment the signal goes live, the field receives a coordinated ask rather than a scattered one. You also review how your agentic mining agent performed on last week's signals, tuning its prioritization so the highest-value patterns rise to the top automatically.
Later in the afternoon, you pull together a metrics read-out for leadership. You show pipeline influenced by activation, signal resolution time, and how differentiated targeting is outperforming broadcast on both engagement and deal size. You call out one activation exceeding targets and recommend scaling it, and you flag a signal source that is generating noise rather than value and propose refining its threshold.
Before wrapping up, you spend time in the field channels where sellers are responding to your latest activation. A few surface customer stories and blockers worth capturing. You feed those back into the mechanism to sharpen the next activation and to ensure each blocker gets a tracked response, closing the loop between what the field told us and what we did about it.
About the team
We are the Data & AI GTM Acceleration team within Amazon Web Services. Our mission is to help every AWS customer realize the value of AI and data services faster. We build and operate go-to-market programs and initiatives that connect the right customer with the right solution at the right moment, at a scale that a traditional sales model cannot reach on its own.
We sit at the center of a broad ecosystem. On one side, we work with AWS product and GTM teams building AI and data services. On the other, we partner with thousands of field sellers, solution architects, and inside sales professionals who bring those services to customers. Our job is to bridge the gap between what the field is telling us and what the field does next, turning signal into coordinated action.
What makes this team unique is how we operate. We build, we prove, and we scale. We prototype new go-to-market mechanisms, validate them with real sellers and real customers, and then hand proven playbooks to enterprise scaling partners who deploy them across the global AWS field. We move quickly, experiment often, and measure everything. We are increasingly AI-native in how we work, building and operating agents that mine field signal and deliver personalized recommendations directly into seller workflows.
Culturally, we are builders obsessed with measurable impact. We operate with a high degree of autonomy, trust each other to own outcomes end to end, and maintain a bias for action even when the path forward is not fully defined. You will work alongside people who are curious, technically fluent, collaborative across organizational boundaries, and energized by the pace of change in AI.
Basic Qualifications
- 5+ years of Go-To-Market, Business Development, Sales, or Consulting experience- 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- 5+ years of in program management, workforce strategy development, or metrics-driven decision making experience
- Bachelor's degree
- Experience selling enterprise software or cloud-based applications
- Experience explaining complex technical concepts to various business and technical audiences
- Experience presenting to both technical and non-technical executive audiences
- Experience leading complex, multi-year initiatives that may be cross-functional and/or span business and technology
Preferred Qualifications
- Master's degree in business, data science, public administration, finance, engineering, human resources, or related field, or PMP certificate- Experience interpreting data and making business recommendations
- Experience identifying, negotiating, and executing complex legal agreements
- Experience interpreting data and making business recommendations across leadership and cross-functional teams
- Experience using analytical tools for workforce metrics and reporting
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, Mountain View - 162,700.00 - 220,200.00 USD annually
USA, CA, San Diego - 147,900.00 - 200,100.00 USD annually
USA, CA, San Francisco - 162,700.00 - 220,200.00 USD annually
USA, GA, Atlanta - 147,900.00 - 200,100.00 USD annually
USA, MA, Boston - 147,900.00 - 200,100.00 USD annually
USA, NY, New York - 162,700.00 - 220,200.00 USD annually
USA, TX, Austin - 147,900.00 - 200,100.00 USD annually
USA, TX, Dallas - 147,900.00 - 200,100.00 USD annually
USA, VA, Arlington - 147,900.00 - 200,100.00 USD annually
USA, WA, Seattle - 147,900.00 - 200,100.00 USD annually
About the company
amazon
Large Enterprise
Amazon is a global leader in e-commerce and cloud computing, founded in 1994 by Jeff Bezos. Initially starting as an online bookstore, it has since expanded its offerings to include a vast range of products and services, including electronics, fashion, and digital content. With Amazon Web Services (AWS), the company also provides powerful cloud solutions to businesses around the world. Known for its innovation, customer-centric approach, and commitment to operational efficiency, Amazon continues to shape the future of retail and technology, consistently seeking new ways to enhance customer experiences.