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Optimum is building the world's first data acceleration network for any blockchain. Powered by Random Linear Network Coding (RLNC), Optimum scales network speed, robustness and throughput by orders of magnitude.
Co-founded by Muriel Médard, co-inventor of RLNC, and a team of industry experts, Optimum introduces a breakthrough in Web3 infrastructure. Our infrastructure enables high-speed data propagation, fast access, and secure updates, scaling the world computer. Backers include 1kx, Spartan, Robot Ventures, Finality Capital, Triton Capital (fka Kraken Ventures), CMT Digital, SNZ and others.
Learn more at getoptimum.xyz
We are looking for a Data Scientist focused on blockchain protocols, MEV, and market structure to lead empirical research that informs Optimum’s networking products and analytics strategy.
The ideal candidate has deep blockchain research experience and can reason from raw on-chain data, protocol behavior, MEV, orderflow, market structure, and network-level timing signals to generate product insights.
You will work at the intersection of networking infrastructure, decentralized systems, and empirical blockchain research, where millisecond-level differences can have meaningful economic consequences.
This is a high-ownership, high-visibility role at an early-stage company. You will shape research methodology, influence product direction, and communicate findings across Product, Research, Economics, and Engineering.
Own empirical research into MEV, block building, orderflow, validator/proposer behavior, latency-sensitive execution, and related market-structure dynamics.
Work with on-chain, mempool, relay, validator, and network-level datasets, including sources such as Xatu, to identify actionable patterns and quantify opportunity areas.
Translate open-ended research questions into rigorous analysis, clear conclusions, and product-relevant recommendations.
Work in an atomic, hypothesis-driven style: formulate clear hypotheses, define verifiable tests, evaluate evidence rigorously, and translate findings into actionable decisions.
Design and evaluate experiments, measurements, and causal analyses for latency-sensitive blockchain systems.
Build analytical frameworks that distinguish signal from noise in complex, high-variance environments.
Develop predictive, statistical, and simulation-based models that answer concrete product, research, and strategy questions.
Document assumptions, limitations, methodology, and results to a high standard.
Partner closely with Product, Economics, Research, and Engineering to turn blockchain research into product direction and strategic insight.
Help define which signals matter, how they should be measured, and how they could become customer-facing product primitives.
Work with Engineering on data pipelines, evaluation tooling, and production-ready research infrastructure.
Communicate complex methodology and market-structure insights clearly to both technical and non-technical audiences.
5+ years of experience in quantitative research, empirical blockchain research, data science, or a similarly analytical role, ideally in crypto, market structure, distributed systems, or latency-sensitive environments.
Strong statistical background and experimental rigor: You are comfortable designing and interpreting experiments (A/B testing, causal inference, quasi-experiments, diff-in-diff, instrumental variables, etc.) and can judge the strength of the evidence
Strong blockchain research expertise: You have experience with Ethereum or another major L1/L2 ecosystem, with hands-on experience conducting empirical on-chain research. You have direct familiarity with topics such as MEV, block building, orderflow, validator/proposer dynamics, transaction propagation, and blockchain market structure.
Experience building predictive and analytical models: This includes regression, classification, time-series analysis, and modern machine learning techniques where appropriate.
Fluency in Python and SQL: Experience working in modern analytical data platforms such as BigQuery, Snowflake, or equivalent.
A product mindset: You can frame ambiguous questions, select rigorous methodologies, and translate research into actionable product and strategic insights.
High autonomy and ownership: You're comfortable defining your own research agenda, communicating complex findings clearly, and operating effectively in a fast-moving, high-ambiguity environment.
Prior work in a research organization, protocol team, crypto trading firm, MEV/searcher team, or infrastructure company.
Familiarity with networking concepts, latency measurement, distributed/decentralized systems, or systems where millisecond differences carry economic weight.
Experience working in an early-stage company where you had to define the problem before solving it.
We are pragmatic about tooling. The following reflects what we currently use or expect, but we care more about fundamentals than any specific tool.
Languages
Python (primary), R
scikit-learn, statsmodels, XGBoost / LightGBM, PyMC or equivalent Bayesian tooling, and/of similar packages
Data Infrastructure
dbt, Spark or equivalent; experience with streaming data a plus
Experimentation
Internal or third-party A/B testing frameworks; familiarity with variance reduction techniques (CUPED, etc.)
Visualization & Reporting
Grafana, Looker, Metabase, or equivalent BI tooling; comfort with ad-hoc Python plotting (Matplotlib, Seaborn, Plotly)
Version Control & Collaboration
Git, Jupyter / Marimo notebooks, Notion or Confluence for documentation
Work on hard problems at the edge of networking, blockchain market structure, and decentralized systems.
Ownership from day one - you will define methodology, not just apply it.
Close collaboration with a small, senior, cross-functional team.
Competitive compensation, equity, and flexibility.
Flexible time off.
Fully remote - work from wherever you do your best thinking. Most of the team operates on ET or CET, so we look for meaningful overlap with those windows.
Don’t Meet Every Requirement?
We still encourage you to apply. We value intellectual curiosity and the ability to learn in context.