Senior Data Scientist

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 Senior Data Scientist to own the analytical and modeling foundations at Optimum. You will work at the intersection of networking infrastructure and decentralized technology — domains where millisecond-level differences have meaningful business impact. This is a high-ownership, high-visibility role at an early-stage company: you will shape methodology, influence product direction, and communicate findings to a broad set of stakeholders including Product, Economics, Research, and Engineering.

• ⁠Own end-to-end methodology for causal inference, identification strategies, and predictive modeling.

• Design, run, and evaluate experiments — from hypothesis formulation through power analysis to result interpretation.

• ⁠Work on product initiatives where data signals are the core deliverable, not just a supporting artifact.

• Partner closely with Product, Economics, and Research as primary stakeholders to translate business questions into rigorous analytical frameworks.

• Work with Engineering on implementation of models and evaluation pipelines, ensuring analytical work is production-ready.

• Produce actionable insights and present complex methodology to both technical and non-technical audiences clearly and concisely.

• Document approaches, assumptions, methodologies, and results to a high standard — building institutional knowledge that scales with the team.

• Postgraduate degree in Statistics, Mathematics, Computer Science, Economics, Data Science, Engineering or a related quantitative discipline.

• ⁠Minimum of 5 years of hands-on experience in a data science or applied research role.

• Strong foundations in causal inference and experimental design (A/B testing, quasi-experiments, diff-in-diff, IV, etc.).

• Proficiency in predictive modeling: regression, classification, time-series, and familiarity with modern ML frameworks.

• Statistical rigor — you know when a result is meaningful and when it isn’t, and you can defend that position.

• ⁠Fluency in Python (pandas, scikit-learn, statsmodels) and SQL; comfort working in cloud data environments (e.g. BigQuery, Snowflake, or equivalent).

• Strong written and verbal communication; you can turn a p-value into a product decision.

• Familiarity with networking concepts and latency measurement — comfort reasoning about systems where millisecond differences carry economic weight.

• Exposure to blockchain or decentralized technology, and understanding of on-chain data structures.

• Experience working in an early-stage or high-ambiguity environment where you have 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)

  • ⁠ ⁠ML / Stats: scikit-learn, statsmodels, PyMC or equivalent Bayesian tooling, XGBoost / LightGBM, R

• ⁠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

• ⁠ ⁠Ownership from day one — you will define methodology, not just apply it.

• ⁠ ⁠Work on genuinely hard problems at the edge of networking and decentralized systems.

• ⁠ ⁠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.