Lead Data Scientist

• Lead Data Scientist – Knowledge Graph & Agentic AI
• River Social | Los Angeles, CA (Remote-First)

Overview

• River is building the individual intelligence layer for the AI era.
• AI is the defining technology of our age, but it has a blind spot. Today's models are trained on the internet, not on you. They don't know what you want, what you value, or how to act on your behalf. River changes that.

We are building the technology that connects AI to the individual – a persistent, portable data identity that makes AI personal, attributable, and valuable. When your data creates value, you share in it. When an AI agent acts, it acts with your authority. When a brand reaches you, it's on your terms.

Our founding CEO brought MTV and NetJets to the European market, and we are backed by a number of technology company luminaries from Google, Sun Microsystems, and others. We are looking for a Lead Data Scientist to take full ownership of everything data science at River – from our personal data graph to our next-generation agentic AI systems. This is a foundational hire that will underpin our expanding team. You will be the person who defines how River understands, connects, and reasons over user data at scale, and who builds the data science function from the ground up.

Come join us on our mission to build individual intelligence, owned by you.

• What We're Building
• River Social – live and growing. A social platform where you own your data, control your identity, and get paid when it's used. 70% of value goes back to you.
• River Source – enterprise consent infrastructure giving AI platforms and brands access to high-fidelity, consented user data at scale.
• RiverAI – launching 2026. A desktop AI client powered by Rivera, River's AI engine: it knows you, acts on your behalf with full transparency and consent, anticipates your needs, and moves with you across platforms.

• Your First 90 Days
• These are the concrete problems on your desk from day one:
• Scale the knowledge graph and its data taxonomy to organize thousands of datapoints per user across millions of users.
• Reduce noise in the data bank – detect and weed out erroneous, duplicate, and low-value data.
• Develop on-device heuristics for detecting relevant datapoints – find the needle in the haystack of user data.
• Reimagine Rivera, our digital assistant – rethink how it reasons over the personal data graph.
• Develop efficient methods for real-time data retrieval from the graph, balancing latency, cost, and accuracy.
• Make a measurable impact on data quality – define the metrics, build the evaluation framework, move the numbers.

Most of this work is greenfield, but it doesn't live in a lab: you'll integrate your solutions into our existing stack and ship. You understand "good enough for now" while still building for future scale, and you reason explicitly about tradeoffs and efficiency.

• Our Stack
• TypeScript across the stack – a React web frontend and Node backend services – with PostgreSQL and pgvector for embeddings and semantic search, Kafka for event streaming, and AWS and GCP cloud infrastructure, with Gemini powering our AI layer today. This is a hands-on role – you'll be working directly in this stack and building on it from day one.

We're a new team, and every new tool carries overhead – we add technology as required, deliberately, when the problem demands it. You should be comfortable building within this stack and making the case when something new is genuinely needed.

• What We're Looking For
• Must have:
• Advanced degree (MS/PhD) in Data Science, Computer Science, Machine Learning, Statistics, or equivalent practical experience.
• 5+ years in applied data science or ML engineering, including end-to-end ML systems in production at scale (>1M daily interactions).
• Experience in a technical leadership role – setting direction, making architectural decisions, and mentoring or leading other engineers or data scientists.
• Strong foundations in classical and modern machine learning – this is not a prompt-engineering role.
• Hands-on experience with knowledge graph construction, entity resolution, and data taxonomy/ontology design across heterogeneous sources.
• NLP – text extraction, named entity recognition, semantic similarity, intent classification, embedding generation.
• Comfortable working with TypeScript and vector databases, and adding new technologies as needed (see Our Stack on how we think about tech discipline).

Strongly preferred:

• Graph-based ML – GNNs, graph embeddings, link prediction.
• Agentic AI architectures – multi-agent systems, tool use, planning, autonomous workflow orchestration.
• Real-time and streaming pipelines (technologies like Kafka or Spark).

Above all:

• A startup mentality – you thrive in ambiguity and are energized by building from scratch.
• Full ownership mindset – if it touches data, models, or intelligence at River, it's yours. You don't wait for specifications.
• Pragmatic judgment – deliberate tradeoffs between speed, quality, and scale, and the ability to articulate why.
• Strong communication – you translate complex technical concepts for non-technical stakeholders.
• A passion for ethical AI and data sovereignty – AI should empower users, not exploit them.

• Why You Should Join
• You will own the entire data science function at River – how our personal data graph learns, how our AI agents reason, and how millions of users experience individual intelligence for the first time. Ground floor, direct line from your work to product and business outcomes, significant potential for wealth creation.

• Logistics
• Location: Remote-first. Occasional in-person team events in Los Angeles; attendance required.
• Hours: Standard business hours are Pacific Time; availability during these hours is essential.
• Authorization: Applicants must be currently authorized to work in the United States and will be required to provide evidence of their right to work.
• References: References will be sought for shortlisted candidates.