Optimum
Senior Data Scientist
1w ago
SeniorRemotedata analysisblockchainempirical research
Lead empirical research focused on blockchain protocols, MEV, and market structure to inform networking products and analytics strategy.
Responsibilities
- 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.
Requirements
- 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.
- We still encourage you to apply. We value intellectual curiosity and the ability to learn in context.
Conditions
- 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.
Other
- 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
- 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 falsifiable 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.
- 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