Data Scientist Jobs

About The Position

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• 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.