inDrive

Global ride-hailing platform with fare negotiation

Senior Applied Researcher - Foundations

Applied Machine LearningUpdated on Nov 13

Full-timeLondon, UKNo salary listedHybrid

About the Job

Requirements

Four to seven years of industry experience in data analytics, data science, or applied research, or a PhD plus two to four years of post-PhD industry experience.
Own individual marketplace models, policies, or decision mechanisms end to end.
Exercise strong judgment in trading off model fidelity, latency, robustness, and business impact.
Move from abstract problem formulation to production-grade decision logic.
Collaborate hands-on with engineering teams on deployment, iteration, and monitoring.
Reason about system behavior under uncertainty, delayed feedback, and partial observability.
Model supply-demand dynamics as stochastic, partially observed, feedback-driven systems; formalize unit economics, service quality, and lifetime value as state variables and objectives.
Design and analyze experiments under interference, spillovers, feedback loops, and adaptive policies, including switchbacks and clustered interventions.
Have practical production experience with difference-in-differences, panel methods, synthetic control, double-robust estimators, and causal machine learning approaches.
Distinguish short-term experimental uplift from long-run equilibrium shifts and cumulative feedback effects using time-series and dynamic causal methods.

Responsibilities

Own well-defined marketplace efficiency problems end to end, from problem formalization through modeling, deployment, and production monitoring.
Conduct in-depth market research and analyze trends to identify opportunities for marketplace growth and optimization.
Develop and refine metrics, dashboards, and key performance indicators to monitor market performance and guide strategic decisions.
Generate and validate hypotheses addressing key market challenges, and present insights and actionable recommendations to stakeholders.
Investigate anomalies and deviations in key metrics, identify root causes, and propose solutions.
Build predictive models and advanced analytics frameworks to forecast market trends and inform resource allocation.
Operate independently on complex problem areas, make explicit trade-offs between competing objectives, and partner closely with engineering to deliver robust, scalable solutions.

Senior Applied Researcher will own well-defined marketplace efficiency problems end-to-end, from problem formalisation through modelling, 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.

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

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.

About the Company

inDrive

Global ride-hailing platform with fare negotiation

inDrive operates a global mobility and urban services platform that connects passengers with independent drivers through a fare-negotiation system. Riders propose a trip price, and nearby drivers can accept, decline, or counteroffer, creating a peer-to-peer ride-hailing model. The service has expanded to intercity rides, delivery and cargo, groceries, advertising, and driver-focused fintech within its app. Unlike typical ride-hailing apps that use fixed fares, inDrive enables price negotiation and direct driver-rider interaction, and it pursues growth through a venture arm that funds ride-hailing-adjacent startups in emerging markets. This broader strategy aims to build a super-app for urban and gig-economy needs, with the goal of offering a wide range of mobility and urban services in 48 countries.

Company size: 10,001+
Company stage: Debt Financing
Total funding: $532M
Headquarters: Mountain View, CA
Founded: 2012

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