Senior Data Scientist - Platform Economics & Simulation
Role details
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Tech stack
Job description
This role is the causal and economic core of that mission. Most analytics can tell you what happened; we're hiring someone who can rigorously explain why value moves, and turn that understanding into sharper decisions. You'll spend your time on two linked problems: explaining the economics of the platform (what drives revenue, retention, and engagement, and what to do about it), and building the simulation and offline-evaluation tooling that lets us test decision policies before we scale them. The team is both reactive and proactive: bringing proposals that turn causal understanding into incremental value, and responding when the platform needs answers.
You'll work with a high degree of autonomy on ambiguous problems, and you'll be measured on the commercial decisions your work changes, not on model metrics alone.
What You'll Do:
- Explain why value moves across revenue, retention, and engagement, through value decomposition, cannibalisation and substitution analysis, meta-analysis across our models, and clear strategic trade-off framing.
- Apply causal reasoning to understand our own models and players: why a model behaves as it does, whether an observed effect is incremental or displaced, and what genuinely drives lifetime value.
- Build offline policy evaluation and simulation tooling that lets the platform identify the content, players, and policies that create incremental value, and stress-test decision policies before they scale.
- Guide model development across the team, raising the standard of causal and economic thinking through review, mentoring, and example.
- Turn causal understanding into concrete proposals, and respond with rigorous answers when the platform needs them.
- Communicate assumptions, methods, and conclusions clearly enough that senior technical and commercial audiences can act on them with confidence.
Requirements
- You think in counterfactuals. You instinctively separate incremental effect from what would have happened anyway, and you reason naturally about substitution, cannibalisation, and the economics of a content platform. A background in economics, econometrics, or a similarly causal quantitative discipline is a strong fit.
- Proven industry experience turning causal and economic understanding into decisions that changed commercial outcomes, in areas such as customer value, retention, pricing, or decisioning.
- Depth in causal methods used to explain and understand systems: treatment-effect estimation, uplift, structural or counterfactual reasoning
- The ability to build, not just analyse: you can implement models, simulation, or offline-evaluation environments and take them to production.
- Strong judgement on when a sophisticated approach is warranted versus a simpler one that answers the question.
- Experience owning ambiguous problems end-to-end: framing the question, choosing the approach, and delivering something the business acts on.
- Fluency with large, complex behavioural datasets and the craft to work with them at scale: Python, SQL, and distributed processing (e.g. PySpark), plus standard ML tooling as the work requires.
- Excellent stakeholder communication: you can make a rigorous causal argument land with a non-technical commercial audience and move a decision.
- A strong quantitative academic background, typically a Master's or PhD.
Nice to Have:
- Experience with incrementality and uplift modelling, or with experimentation frameworks.
- Reinforcement learning or contextual-bandit decisioning, and offline evaluation for recommendation or decisioning systems.
- Background in gaming, e-commerce, or subscription businesses.
- Experience deploying and maintaining business-critical or consumer-facing models.
- Experience working in agile, fast-paced environments.