Answer Engine Optimization Lead Jobs

Asana's Data Science team helps us fulfill our mission by informing strategy, defining success metrics, and identifying new ways to deliver user value . Data scientists are at the crux of deepening our understanding of the customers and driving more business outcomes by leveraging experimentation, causal inference, statistical and machine learning techniques, and data storytelling . As an Analytical Engineering Manager, you lead a team of Analytical Engineers who own the data foundations for the business: the Gold layer, canonical metrics, certified dashboards, and semantic layer that make Asana's most important numbers trustworthy, and that make AI-powered self-serve through Claude and Databricks Genie actually work. You sit at the intersection of Data Engineering, Analytics, and Data Science, and you are accountable for whether business stakeholders trust the data in your team's domains and can answer their own questi ons without routing through your team. This role is based in our Warsaw office with an office-centric hybrid schedule . The standard in-office days are Monday, Tuesday, and Thursday . Most Asanas have the option to work from home on Wednesdays . Working from home on Fridays depends on the type of work you do, and your recruiter can share more about the in-office requirements . We offer a Contract of Employment (UoP) for our employees in Poland. What you’ll achieve Lead, grow, and develop a team of Analytical Engineers: Own hiring, coaching, performance, and career growth, setting a high bar for data-model quality and stakeholder trust. Own the Gold layer and semantic-layer strategy across your team's domains (e.g. PLG, marketing, revenue, NPI/AWM), taking accountability for curated data models, canonical metrics, dashboards, and Genie spaces. Treat every recurring insight as a product with an owner, a cadence, and an SLA, building a catalog of trusted, versioned data products instead of one-off rebuilds. Drive self-serve enablement by prioritizing Go

Data Scientist The Data Science team helps us fulfill our mission by informing strategy, defining success metrics, and identifying new ways to deliver user value . Data scientists are at the crux of deepening our understanding of the customers and driving more business outcomes by leveraging experimentation, causal inference, statistical and machine learning techniques, and data storytelling . We understand our company’s goals and proactively inform their direction with data so that our product can help more teams do great things. As a Data Scientist at Asana, you’ll help us ask the right questions and answer them rigorously. You’ll work closely with our Pro duct and Business teams to understand their goals and proactively inform their direction with data. You’ll keep taking on new responsibilities as you grow—from defining core metrics to building machine learning models and keeping the data flowin g in our pipelines This role is based in our Warsaw office with an office-centric hybrid schedule . The standard in-office days are Monday, Tuesday, and Thursday . Most Asanas have the option to work from home on Wednesdays . Working from home on Fridays depends on the type of work you do, and your recruiter can share more about the in-office requirements . We offer a Contract of Employment (UoP) for our employees in Poland . What you’ll achieve Design and analyze experiments to measure the impact of new product features. Investigate high-level questions like “What are the collaborative patterns of the most successful teams using Asana?” Add new metrics and aggregations to our data warehouse to make new classes of questions answerable. Build models to predict the growth trajectory of different customer segments. Partner with cross-functional stakeholders across engineering, product management, and business teams to drive data-informed decision-making. About you Bachelor's Degree in Computer Science, Math, Statistics, Engineering, a related quantitative field, or equivale

Every large organization has the same problem: too many projects, not enough visibility into who's actually available to do the work. Operations teams, PMOs, and professional services firms are making staffing decisions in spreadsheets, guessing at utilization, and finding out they're over-capacity after it's too late. Resource Management (RM) is Asana's answer to that problem — and we're at an inflection point in how we think about it. RM has strong foundations and proven enterprise demand, but it was built for a pre-agentic world. The next version of this product looks fundamentally different: AI Teammates that monitor utilization in real time, flag staffing risk before it hits delivery, recommend rebalancing across teams and projects, and automate the operational overhead that makes resource planning so painful today. We believe this capability is worth investing in seriously — not just as a feature within Asana's broader platform, but potentially as a standalone commercial offering for enterprise buyers whose primary need is resource visibility, not work management. As a Staff Product Manager leading this reinvention, you'll own the full RM product strategy — global capacity planning, skills-based staffing, time tracking, utilization reporting, and the agentic workflows that make all of it proactive rather than reactive. The strategic questions are genuinely open: what does RM look like as a reimagined agentic product, who is the right buyer, and how do we price and position it as a commercial offering in its own right? This role shapes those answers. This role is based in our New York office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. W

The Dashboards product is Datadog's unified single-pane-of-glass for metrics, logs, and traces—a comprehensive treasure trove of observability data. We are transforming Dashboards into an AI-native control surface and the central hub where every team moves seamlessly from question to insight to action – providing a guided experience that feels like having an expert SRE at your side and ensuring the entry point is never an empty canvas. We're hiring a Staff Applied Scientist to define and guarantee the quality of this AI system at scale. "Good" isn't one number — it spans answer quality, tool-selection accuracy (critical given the growing catalog of data sources and visualizations), retrieval relevance, latency, token cost, and end-to-end agent success. The space is full of open questions. How do you evaluate an agent end-to-end when the trajectory is non-deterministic? How do you score tool selection when a user’s query can result in the agent making decisions against dozens of visualizations and data sources – both of which are growing month over month? How do you build a measurement system that catches regressions across all widget types and data sources (e.g., enforcing correct grouping, sorting, and time overrides), and is easy to use and extend by dozens of teams? If those are the problems you want to spend your time on, come build this with us. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Own the evaluation strategy for Dashboards, as well as sister teams within our organization. Define the metrics — offline and online, quality and cost, single-turn and multi-turn — that the team and the broader organization optimize against. Build the eval datasets, golden traces, and regression harnesses that catch quality changes before they hit customers, an

Team description At Datadog, AI agents are becoming first-class consumers of observability, security, and software delivery data — from third-party coding agents like Claude Code, Cursor, and Copilot, to our own Bits SRE, Bits Assistant, and Bits Dev Agent. The Agentic Interfaces team owns the platform that connects these agents to Datadog: the MCP Server, the tools and retrieval surfaces agents call into, and — critically — the evaluation systems that tell us whether an agent's experience on Datadog data is actually getting better over time. This role is about that last piece. We're hiring a Staff Applied Scientist to define what "good" means for an Agentic interface at Datadog and to build the measurement systems that make it true. "Good" isn't one number — it spans answer quality, tool-selection accuracy, retrieval relevance, latency, token cost, and end-to-end agent success on real customer workflows. You'll design the evals, build the datasets, define the metrics, and partner with the AI engineers on the team to land the platform that lets every product group at Datadog ship integrations that are demonstrably better release over release. The space is full of open research questions. How do you evaluate an agent end-to-end when the trajectory is non-deterministic? How do you score tool selection when the tool catalog has hundreds of entries and grows weekly? How do you build a measurement system that catches regressions across first-party and third-party agents at once, without each team writing their own harness? If those are the problems you want to spend your time on, come build this with us. Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. What You’ll Do: Own the evaluation strategy for Datadog's AI agent integrations. Define the metrics — offline and online, quali

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Figma's platform helps teams bring ideas to life — and Data Science is central to how we measure, improve, and scale it. We're on a mission to make data self-serve for every Figmate: moving from a world where every data question requires filing a request to a DS or DE, to one where anyone can ask a question in plain language and get a trustworthy answer in minutes. We're hiring Figma's first dedicated Technical Program Manager for Data Science to own that mission — driving our multi-phase Self-Serve Analytics program from semantic layer expansion through the Figma Data Assistant, while building the operational foundation for a growing DS org. This is a full time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma: Own end-to-end program delivery for Figma's Self-Serve Analytics initiative that includes semantic layer expansion, Slack bot launch, automated eval framework, and the Figma Data Assistant Coordinate delivery across Data Engineering, DS ICs, Security, IT, and business stakeholders (Sales, Product, Finance, Research) to expand self-serve data coverage and drive adoption Lead the governance and security work stream: work with Security and IT on LLM data access policies, PII guardrails, access controls, and LLM provider approvals Drive adoption and change management — build champion networks, run training programs, and meet Figmates where they work (Slack, Notion) to shift from request-based to self-serve data culture Build and maintain

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The strongest enterprise and mid-market marketing doesn't lead with a product — it leads with the problem a buyer already has. A hospital administrator isn't looking for "Lyft Concierge"; they're looking to reduce missed appointments. An auto dealer principal isn't looking for a rideshare product; they're looking to keep loaner logistics from costing them revenue. We are in the process of revising our go-to-market for Lyft Business to organize around these audiences and their transportation problems, with Concierge, Lyft Pass, Business Profiles, and our Business Travel products positioned as the answer to a clearly named pain point by customer profile. We're hiring an Industry Marketing Manager to build that audience-first structure for our priority verticals, starting with Healthcare and Automotive , across both mid-market and enterprise segments. You'll own the vertical narrative and the content, demand generation strategy, and enablement that will drive marketing-attributable revenue. The primary function of this role is to build and lead vertical-specific go-to-market (GTM) motions for our mid-market and Enterprise business — bringing together vertical-specific content, demand generation, and sales enablement into a coherent, audience-first strategy. Responsibilities: Own vertical strategy and integrated programs Design and execute large, orchestrated tentpole programs for our priority verticals (no more than 2 per quarter) — multichannel activation spanning digital, live events, paid media, social, and press — that bring our value proposition and industry point of view to life for a defined buyer (e.g., hospital CFO, healthcare operations leader, dealer principal/service manager). Build and maintain a thought leadership agenda for each prioritized vertical: original research, proof p

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The strongest enterprise and mid-market marketing doesn't lead with a product — it leads with the problem a buyer already has. A hospital administrator isn't looking for "Lyft Concierge"; they're looking to reduce missed appointments. An auto dealer principal isn't looking for a rideshare product; they're looking to keep loaner logistics from costing them revenue. We are in the process of revising our go-to-market for Lyft Business to organize around these audiences and their transportation problems, with Concierge, Lyft Pass, Business Profiles, and our Business Travel products positioned as the answer to a clearly named pain point by customer profile. We're hiring an Industry Marketing Manager to build that audience-first structure for our priority verticals, starting with Healthcare and Automotive , across both mid-market and enterprise segments. You'll own the vertical narrative and the content, demand generation strategy, and enablement that will drive marketing-attributable revenue. The primary function of this role is to build and lead vertical-specific go-to-market (GTM) motions for our mid-market and Enterprise business — bringing together vertical-specific content, demand generation, and sales enablement into a coherent, audience-first strategy. Responsibilities: Own vertical strategy and integrated programs Design and execute large, orchestrated tentpole programs for our priority verticals (no more than 2 per quarter) — multichannel activation spanning digital, live events, paid media, social, and press — that bring our value proposition and industry point of view to life for a defined buyer (e.g., hospital CFO, healthcare operations leader, dealer principal/service manager). Build and maintain a thought leadership agenda for each prioritized vertical: original research, proof p

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The People Analytics team at Lyft exists to build data trust — turning people data into clear, reliable insights that help our HR Business Partners, talent leaders, and executives make better decisions. We're a small, high-impact team in the middle of a meaningful BI transformation: we recently moved to ThoughtSpot as our primary analytics platform, and we're not just replicating old dashboards — we're rethinking what self-service analytics looks like for a People team. That includes exploring ThoughtSpot's AI capabilities to surface proactive insights, enable natural language querying, and reduce the friction between a business question and a data answer. There's real greenfield work here, and we're looking for someone who wants to help define what AI-powered people analytics looks like at Lyft. We're looking for a Data Analyst to join our People Analytics team, based in Toronto. You'll report to the Senior Manager, People Analytics and serve as the first point of contact for incoming data requests across the People organization — triaging, scoping, and delivering on analytical needs ranging from quick ad-hoc pulls to fully built ThoughtSpot dashboards. This role is well-timed: you'll be joining as we complete our migration from Tableau to ThoughtSpot, which means you'll have genuine influence over how we build our reporting layer and push into ThoughtSpot's AI features — think natural language search, AI-generated insights, and proactive anomaly detection applied to people data. This isn't a "maintain the dashboard" role. It's a chance to help build something new. You'll be a self-starter who is equally comfortable writing SQL and presenting findings to a VP. Responsibilities: Serve as the first point of contact for incoming data requests from across the People organization — scoping needs, setting

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: At Airbnb, we're driven by our mission to create a world where anyone can belong anywhere. The Community Support (CS) Platform & Engineering team is at the forefront of this mission - building the AI systems that power world-class support experiences for millions of guests and hosts globally. We are building to a bold vision, where our AI doesn't just answer questions, but truly understands each person's unique context, mirrors the judgment and empathy of our highest-performing human agents, and delivers 10-Star support at scale. The AI Personalization team is the intelligence layer that makes this possible. We own the full-stack systems that enable the AI to give every guest and host a response that feels written just for them. The Difference You Will Make: We are looking for a visionary Staff Platform Manager to lead the product strategy and execution for AI Personalization across Airbnb's customer support ecosystem. This is one of the most consequential roles in CS Platform Product: you will own the full stack of personalization, from the underlying retrieval systems to the consumer-facing AI experience, and scale it across multiple Airbnb surfaces. The north star: enable AIA to emulate the judgment, empathy, and accuracy of Airbnb's highest-performing human agents. To achieve this, you'll build a platform that continuously enriches the AI's understanding of each user, and surfaces that understanding at the right moment, in the right form, across every channel. This role demands a rare combination: customer experience chops to build meaningful and delightful perso

Role Description We are seeking a Senior Manager, Data Engineering to lead the team responsible for Dropbox’s underlying data foundations that power our business as a whole. This is a hands-on engineering leader who owns the pipelines and data products that Product, GTM, Finance, and the CTO organization depend on to make decisions. In this role, you will lead and grow a team of data engineers building and operating our ingestion, transformation, orchestration, and serving layers, as well as the self-serve analytics substrate that lets partner teams answer their own questions without bespoke engineering work. The ideal candidate is a deeply technical, product-minded engineering leader who can hold a high bar on system reliability and data quality while partnering closely with Data Science, Business Intelligence Engineering, Analytics, and Product to turn fragmented, ticket-driven data work into durable, reusable data products. Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here . Responsibilities Data Quality & Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics. Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy. Cost & Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability. Cross-Functional Partnership: Partner deeply with Data Science, BIE, Analytics, Product, Data Platform, and the CTO org to define the semantic layer, modeling standards, and data contracts that make downstream work trustworthy and fast. Engineering Culture: Establish rigorous engineering practices

Role Description We are seeking a Senior Manager, Data Engineering to lead the team responsible for Dropbox’s underlying data foundations that power our business as a whole. This is a hands-on engineering leader who owns the pipelines and data products that Product, GTM, Finance, and the CTO organization depend on to make decisions. In this role, you will lead and grow a team of data engineers building and operating our ingestion, transformation, orchestration, and serving layers, as well as the self-serve analytics substrate that lets partner teams answer their own questions without bespoke engineering work. The ideal candidate is a deeply technical, product-minded engineering leader who can hold a high bar on system reliability and data quality while partnering closely with Data Science, Business Intelligence Engineering, Analytics, and Product to turn fragmented, ticket-driven data work into durable, reusable data products. Responsibilities Data Quality & Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics. Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy. Cost & Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability. Cross-Functional Partnership: Partner deeply with Data Science, BIE, Analytics, Product, Data Platform, and the CTO org to define the semantic layer, modeling standards, and data contracts that make downstream work trustworthy and fast. Engineering Culture: Establish rigorous engineering practices — code review, testing, CI/CD for data, incident response, and postmortems — and champion the effective, measured use of AI coding tools to improve engineering productivity. Team Leadership: Lead, mentor, and grow

As a Staff Application Security Engineer at Datadog, you'll set technical direction for how we approach application security at scale. You'll define the frameworks, methodologies, and architectural patterns that engineering teams across Datadog adopt and apply independently. You're the person others come to when they don't know how to make something secure, and you reliably have an answer. You'll be a point of contact for our most complex security programs, often spanning multiple teams and multiple quarters. The role requires both depth (going very deep on specific problems when needed) and breadth (recognizing patterns across systems and drawing connections that others miss). Partnering closely with teams inside and outside the security org is key to success. You'll help shape the AppSec roadmap and make the case for where investment should go. We use our own platform. Logs, Dashboards, Service Catalog, and APM aren't just things we sell: they're tools the AppSec team uses to build security services, measure adoption of secure defaults, and communicate risk across the organization. AI is also part of the picture. Engineering at Datadog increasingly uses agentic tooling throughout the development lifecycle, and many of the products we ship to customers now include AI-powered features. Both create new attack surfaces, and defining our strategy for addressing them is part of this role. If using Datadog to observe Datadog's own security posture, building impactful tooling, and shaping how we secure AI-powered systems sounds like the right kind of problem, this role is worth a close look. What You’ll Do: Define and drive security standards and secure-by-default solutions, serving as the Application Security subject matter expert. Build security tooling and automation that scales security practices across engineering teams, and implement robust security observability to support our threat detection team with meaningful, actionable security signals. Lead threat mod

Who we are About Stripe Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. What you’ll do Responsibilities Build statistical models, define and analyze product and operational metrics, explore experimental design, and construct exploratory analysis with internal data. Work closely with product and business teams to identify important questions and answer them with data. Collaborate with other data scientists, engineers and operations to formulate innovative solutions to experiment and implement advanced data mining techniques. Conduct exploratory analysis on internal data to understand user behavior to inform product development. Drive the collection of new data and the refinement of existing data sources. Apply statistical and machine learning models on large datasets to measure results and outcomes, and identify causal impact and attribution. Predict future performance of users or products. Define, measure, and monitor key outcome metrics for teams and support Stripe’s business. Communicate complex concepts and the results of metrics and analyses in a clear and effective manner through creative visualization. Communicate findings broadly and interact with other teams including product managers, software engineers, marketing, and business development. Who you are Minimum requirements Must have a Master's degree or foreign equivalent in Operations Research, Statistics, Industrial Engineering, Business Analytics, Mathematics or a related field, plus three (3) years of

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team We are building an elite team to drive financial insight and strategy at Stripe. The Finance & Strategy team brings understanding and predictability to Stripe’s financials, and we work together with our key business partners to accelerate long term growth for Stripe. What you’ll do Responsibilities Partner with F&S and Business Leaders to drive the long-term success of Stripe Work cross functionally to identify important strategy questions and answer them with data Build financial forecasts and targets, track performance, and understand deviations from actuals to drive strategy and operational rigor Gain deep knowledge and understanding of Stripe's business performance across geographies and user segments Define metrics and leading indicators of business performance and partner with the Analytics team to develop tools and dashboards to make these metrics widely accessible across Stripe Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 7–14 years of experience in a relevant analytical or finance role Intermediate (at a minimum) or advanced knowledge of SQL or Python is required - you must be well versed working with extremely large and complex datasets + extracting insights Experience with financial analysis, financial forecasting, BvA (vari