Deepgram is the real-time API platform powering the trillion-dollar Voice AI economy.

Backed by a $130M Series C at a $1.3B valuation, Deepgram is trusted by 200,000+ developers and 1,300+ organizations to build Voice AI products, platforms, and autonomous agents with the lowest latency, highest accuracy, and enterprise reliability.

Our voice-native foundation models and runtime infrastructure have processed 50,000+ years of audio and over 1 trillion words, making Deepgram the most experienced voice AI platform in the world.

Industry-leading models & platform:
👂 Nova-3 — the world’s most accurate real-time speech-to-text model
🔊 Aura-2 — professional, enterprise-grade text-to-speech
💬 Flux — the first Conversational Speech Recognition model designed to handle interruptions
🚀 Voice Agent API — enterprise-ready, real-time conversational AI
🧠 Saga — the Voice OS

Beyond core infrastructure, Deepgram is expanding the Voice AI ecosystem through:
💪 Powered by Deepgram, supporting voice products built by leading AI startups and enterprise organizations
🌉 A new Voice AI Collaboration Hub in San Francisco for builders, partners, and the voice community
🍔 The acquisition of OfOne, delivering real-time Voice AI for restaurants and drive-thru operations with 95%+ containment
📃 A growing patent portfolio in Voice AI

Much like APIs powered the payments and cloud economies, Deepgram is building the foundation for a trillion-dollar B2B Voice AI economy—centered on the most natural human interface: voice.

As the founding analyst, you'll establish the analytics foundation for our product team—building pipelines, defining frameworks, and creating the systems that help product managers, designers, and engineers make faster, and more impactful decisions. You'll start with a laser focus on our self-serve funnel, driving conversion from signup to first programmatic API/SDK request, knowing that this optimization directly feeds both self-serve revenue and sales-qualified leads. As you establish this foundation, your scope will expand to help identify patterns across our multi-product portfolio and inform strategic investment decisions.

What You'll Do

Own the analytics strategy for self-serve growth—design experiments, build measurement infrastructure, and drive insights that increase conversion from signup to first programmatic request

Build robust, repeatable analytics pipelines and dashboards from the ground up using Superset and Heap Analytics

Partner directly with product managers and designers to define success metrics, establish baselines, and instrument products correctly

Design and analyze experiments across the developer journey, with deep understanding of API/SDK adoption patterns

Collaborate with analytics partners in Marketing and Sales to connect product usage signals with acquisition, activation, and downstream sales outcomes, building a holistic view of the end-to-end PLG funnel

Surface patterns in product usage across our growing portfolio to identify high-value use cases, customer segments, and where to double down on investment

Serve as the analytical partner to Product leadership: synthesize insights across multiple products and use cases to identify patterns that inform longer-term product strategy and investment decisions

Establish experimentation best practices and analytical rigor as the team scales PLG motions

Translate complex findings into clear, actionable recommendations that shape product roadmap and investment decisions

Build the analytics muscle for the team—creating frameworks and systems that enable PMs to self-serve insights while you focus on strategic analysis

It's Important to Us That You Have

Staff-level expertise in product analytics, with proven ability to lead analytics strategy (we care more about expertise and leadership than years of experience)

Demonstrated track record of accelerating self-serve or PLG growth at a previous company, particularly driving conversion in developer-focused products

Strong SQL skills and hands-on experience working directly with data warehouses

Proven ability to build dashboards and analytics infrastructure yourself—you don't need a data engineering team to be effective

Deep experience designing, running, and analyzing experiments with statistical rigor

Understanding of developer behavior and API/SDK adoption patterns—you know what signals matter for programmatic usage

Experience being the first or founding analyst on a team—comfortable building everything from scratch

Leadership presence to establish best practices and mentor PMs on data literacy

Excellent communication skills to translate technical findings into strategic recommendations for executives

It Would Be Great If You Had

Experience with Superset and Heap Analytics (or similar tools like Mixpanel, Amplitude, Looker, Mode)

Background in B2B SaaS with both self-serve and enterprise motions

Understanding of multi-product portfolio dynamics and how to measure cross-product patterns

Experience with usage-based pricing models or real-time streaming data products

Deep knowledge of product instrumentation and event tracking best practices

Expertise in advanced statistical methods beyond basic A/B testing (causal inference, cohort analysis, time-series analysis)

Experience working in voice AI, ML/AI platforms, or developer tools