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inDrive is seeking a Senior Applied Researcher to own well-defined marketplace efficiency problems end-to-end, from problem formalization through modeling, deployment, and production monitoring. You will operate independently on complex problem areas, make explicit trade-offs between competing objectives, and partner closely with engineering to deliver robust, scalable solutions.
Key responsibilities include:
- Conduct in-depth market research and analyze trends to identify opportunities for marketplace growth and optimization
- Develop and refine metrics, dashboards, and KPIs to monitor market performance and guide strategic decisions
- Generate and validate hypotheses to address key market challenges, presenting insights and actionable recommendations to stakeholders
- Investigate anomalies and deviations in key metrics, identifying root causes and proposing solutions
- Build predictive models and advanced analytics frameworks to forecast market trends and inform resource allocation
This role requires deep expertise in marketplace dynamics, causal inference, and production-grade analytics. You will reason about system behavior under uncertainty, delayed feedback, and partial observability. The ideal candidate has modeled supply-demand dynamics as stochastic, partially observed, feedback-driven systems, and formalized unit economics, service quality, and LTV as state variables and objectives. You will design and analyze experiments under interference, spillovers, feedback loops, and adaptive policies (e.g., switchbacks, clustered interventions). You must distinguish short-term experimental uplift from long-run equilibrium shifts and cumulative feedback effects using time-series and dynamic causal methods.
Requirements:
- 4–7 years of industry experience in data analytics, data science, applied research, or PhD + 2–4 years post-PhD in industry
- End-to-end ownership of individual marketplace models, policies, or decision mechanisms
- Strong judgment in trading off model fidelity, latency, robustness, and business impact
- Proven ability to move from abstract problem formulation to production-grade decision logic
- Hands-on experience collaborating with engineering teams on deployment, iteration, and monitoring
- Demonstrated ability to reason about system behavior under uncertainty, delayed feedback, and partial observability
- Modeling supply-demand dynamics as stochastic, partially observed, feedback-driven systems; formalizing unit economics, service quality, and LTV as state variables and objectives
- Design and analysis of experiments under interference, spillovers, feedback loops, and adaptive policies (e.g., switchbacks, clustered interventions)
- Practical experience with DiD, panel methods, synthetic control, double-robust estimators, and causal ML approaches in production settings
- Ability to distinguish short-term experimental uplift from long-run equilibrium shifts and cumulative feedback effects using time-series and dynamic causal methods
