About Yulu

nYulu is India’s largest shared electric mobility-as-a-service company. Yulu’s mission is to reduce traffic congestion and air pollution by running smart, shared, and small-sized electric vehicles. Yulu is led by a mission-driven & seasoned founding team and has won several prestigious awards for its impact and innovation.

Yulu is currently enabling daily commuters for short-distance movements and helping gig-workers deliver goods for the last mile with its eco-friendly rides at pocket-friendly prices and reducing carbon footprint.

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nYulu is excited to welcome people with high integrity, commitment, the ability to collaborate and take ownership, high curiosity, and an appetite for taking intelligent risks. If our mission brings a spark into your eyes and if you’dlike to join a passionate team that’s committed to transforming how people commute, work and explore their cities - come, join the #Unstoppable Yulu tribe!

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nStay updated on the latest news from Yulu at https://www.yulu.bike/newsroom and on our website, https://www.yulu.bike/.

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nRole Summary

nOptimization & Decision Science

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- Formulate ambiguous business problems as mathematical optimisation and sequential decision-making problems under real-world operational constraints.
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- Design scalable algorithms for matching, allocation, routing, scheduling, pricing, and resource optimisation across multi-objective environments.
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- Develop optimisation frameworks that maximise long-term system efficiency, business value, and user experience while balancing competing objectives.
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nMachine Learning & Artificial Intelligence

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- Design, develop, and deploy production-grade machine learning systems across supervised, unsupervised, self-supervised, probabilistic, and reinforcement learning paradigms.
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- Develop robust feature representations and scalable learning architectures capable of operating on structured, temporal, spatial, graph, and high-dimensional datasets.
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- Improve model performance through principled experimentation, rigorous validation, uncertainty quantification, and continuous model adaptation.
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nStatistical Learning & Predictive Analytics

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- Develop statistically rigorous predictive models for forecasting, estimation, behavioural modelling, anomaly detection, risk assessment, recommendation, and ranking.
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- Apply statistical inference to quantify uncertainty, estimate causal effects, validate model assumptions, and support evidence-based decision-making.
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- Design models that remain calibrated, interpretable, and robust under changing data distributions and operational environments.
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nTime Series Intelligence

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- Build forecasting systems for demand, supply, pricing, utilisation, capacity planning, and operational performance across multiple temporal and spatial resolutions.
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- Develop probabilistic forecasting models capable of modelling trend, seasonality, uncertainty, structural breaks, and external drivers.
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- Design adaptive forecasting pipelines capable of continuously learning from evolving data streams.
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nGraph Machine Learning & Network Intelligence

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- Model complex relational systems using graph representations and network analytics.
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- Develop graph-based learning algorithms for recommendation, matching, fraud detection, routing, community discovery, knowledge graphs, and network optimisation.
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- Apply graph embeddings, graph neural networks, and representation learning to large-scale relational datasets.
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nReinforcement Learning & Sequential Decision Systems

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- Develop intelligent decision-making systems where actions influence future system behaviour.
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- Design algorithms that optimise long-term rewards under uncertainty while balancing exploration and exploitation.
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- Apply reinforcement learning, contextual bandits, online learning, and sequential optimisation techniques where conventional supervised learning is insufficient.
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nGeospatial Intelligence

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- Develop machine learning solutions over spatial and spatio-temporal datasets.
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- Model mobility patterns, spatial interactions, network coverage, route optimisation, and geographic demand dynamics.
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- Build scalable geospatial representations that power prediction, optimisation, and operational decision-making.
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nExperimentation, Causal Inference & Scientific Evaluation

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- Design statistically rigorous online and offline experiments to evaluate product, operational, and marketplace interventions.
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- Estimate causal impact beyond conventional A/B testing while identifying confounding, selection bias, and treatment heterogeneity.
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- Translate experimental findings into deployable decision frameworks with measurable business outcomes.
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nApplied Mathematics & Scientific Computing

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- Apply principles from probability, statistics, optimisation, numerical methods, information theory, and linear algebra to derive efficient algorithms.
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- Analyse computational complexity, convergence, numerical stability, approximation quality, and scalability of mathematical models.
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- Develop mathematically principled solutions rather than relying solely on off-the-shelf machine learning techniques.
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nResearch & Scientific Innovation

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- Continuously evaluate advances in machine learning, optimisation, artificial intelligence, operations research, and statistical learning.
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- Prototype, benchmark, and productionise novel algorithms where they provide measurable improvements over existing approaches.
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- Drive innovation through first-principles thinking, scientific experimentation, empirical validation, and rigorous quantitative analysis.
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nQualifications

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- Strong foundation in probability theory, statistical inference, linear algebra, multivariable calculus, numerical optimisation, information theory, and statistical learning theory.
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- Demonstrated expertise in optimisation, machine learning, predictive analytics, graph machine learning, reinforcement learning, time series forecasting, and decision science.
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- Experience designing and deploying production machine learning systems that operate reliably at scale and deliver measurable business impact.
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- Proven ability to translate ambiguous business problems into mathematically rigorous models, scalable algorithms, and production-ready intelligent systems.
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- Expert-level programming skills in Python and SQL with strong software engineering practices and experience writing productive, maintainable, and production-quality code.
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- Excellent analytical, problem-solving, and communication skills with the ability to collaborate effectively across engineering, product, and business teams.
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📌 Data Scientist (Bengaluru)
🏢 Yulu
📍 Bengaluru