Network Scientist, Polaris
Listed on 2026-02-17
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IT/Tech
Data Scientist, Systems Engineer, AI Engineer
About Protocol Labs
Protocol Labs is an innovation network of 750+ teams, projects, and movements with a common mission:
To drive breakthroughs in computing to push humanity forward. For us, decentralization is more than just a rallying cry; it’s an operational ethos and a belief system baked into the products we develop. Protocol Labs operates as an innovation network built to help breakthrough technologies move from bold R&D into real-world adoption. Through funding, mentorship, and deep network support, PL has enabled pioneering teams to cross the critical “R&D chasm” and bring transformative products to market.
Our network has supported projects like IPFS, Filecoin, and libp2p, alongside emerging efforts in neurotech, AI, and digital rights, demonstrating the breadth of impact possible when world‑class founders and investors connect through Protocol Labs. Our key focus areas include digital human rights, upgrading economies and governance systems, safe AI, and neurotech. For more information on Protocol Labs and our focus areas, please watch Juan Benet’s talk from PL Summit 2023 and the 2024 PL Summit Video (Remote Update).
Polaris
As an innovation network rooted in a culture of collaboration, Protocol Labs requires a fresh approach to advancing emerging technologies. Polaris builds the infrastructure that enables Protocol Labs teams, projects, and movements, and members to flourish. We create the programs, tools, and events that connect, strengthen, and build the teams that drive breakthroughs in computing to push humanity forward. We manage network membership and track network data to continuously gauge the growth and state of maturity of Protocol Labs across a number of critical pillars, and manage the Protocol Labs brand and strategic marketing initiatives.
At the core of everything Polaris does, is the Protocol Labs mission and the desire to progress innovation.
The Network Scientist is a foundational role within Polaris, reporting directly to the Chief Product Officer (CPO). This person acts as the methods authority by charter, responsible for the rigor, reproducibility, and predictive modeling that underpin how the Protocol Labs Network is designed, measured, and improved.
The Network Scientist will define the network’s key metrics, such as Innovation Units (IU) and Expected ΔIU per $ – and ensure that all interventions (features, programs, and funding mechanisms) are measurable and comparable on a unified basis. They will chair the Experiment Review Board (ERB), govern Pre-Analysis Plans (PAPs), and partner closely with the Director of Instrumentation & Experiments to operationalize experimentation and modeling infrastructure.
This role sits at the intersection of network science, causal inference, and product strategy, applying computational social science and economic principles to engineer the world’s first large-scale, self‑improving innovation network.
Key Responsibilities- Define and govern network metrics. Own the Metric Dictionary, mapping surrogates (e.g., company formation, citations, adoption, follow‑on capital) to Innovation Units and updating priors/posteriors as evidence accumulates.
- Lead network‑aware experimentation. Design and approve PAPs; ensure integrity through cluster or saturation randomization, interference‑robust estimators, and fair power/MDE thresholds.
- Model and simulate the network. Build and continuously refine an agent‑based / graph model of the Protocol Labs Network to produce ΔIU forecasts, test counterfactuals, and guide portfolio allocation.
- Publish the State of the Graph. Produce monthly and quarterly reports on network structure, diffusion, and IU per dollar, highlighting learnings and recommendations for CPO and SAB review.
- Safeguard methodological integrity. Enforce experiment guardrails (integrity, fairness, and anti‑gaming), and elevate issues to the Scientific Advisory Board as needed.
- Co‑design mechanisms. Partner with the CPO and Program Leads to model and stress‑test mechanisms such as admissions, reviewer markets, and alignment‑asset reward rules.
- Build internal literacy. Translate complex causal models into actionable…
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