Senior Data Scientist | Map

At Snapp, we’re redefining how cities move. Our ride-hailing and mobility platform connects millions of riders and drivers every day, delivering safe, reliable, and efficient transport solutions. Powered by real-time data and robust infrastructure, we make urban travel faster, simpler, and more sustainable.

We operate with the mindset of a global tech leader and the agility of a startup, building services that scale across markets while staying responsive to local needs.

As a Senior Data Scientist at Snapp, you will lead the formulation and analysis of complex, real-world decision problems across the marketplace, applying rigorous statistical and machine-learning foundations to influence product and operational decisions at scale.

While your scope spans general decision science, experimentation, and modeling, a key focus area for this role is Map & Location intelligence—improving location accuracy, robustness, and reliability within a high-impact, production-grade environment that directly affects matching quality, ETA, and customer experience.

You are expected to operate as a senior individual contributor: shaping analytical direction, challenging assumptions, and ensuring that models are not only correct, but usable, explainable, and robust in production.

• Lead the design, development, and validation of statistical and machine-learning models that support large-scale decision-making across the marketplace.

• Formulate ambiguous, real-world problems using probabilistic reasoning, optimization, and causal thinking, accounting for uncertainty, constraints, and behavioral responses.

• Own Map & Location–related problem spaces, including (but not limited to):

  • GPS accuracy improvement and noise reduction

  • Map-matching and trajectory inference

  • Detection of anomalous or unreliable location signals

• Design and analyze experiments and quasi-experiments (A/B tests, offline evaluations, counterfactual analysis) to measure the impact of product and policy changes.

• Translate analytical insights and prototypes into production-ready logic, collaborating closely with backend and data engineering teams.

• Develop interpretable models and diagnostics that explain mechanisms, trade-offs, and failure modes—especially in high-stakes systems like location and matching.

• Define, monitor, and evolve metrics that reflect system health, user experience, and long-term marketplace stability.

• Act as a statistical and modeling authority, reviewing analytical approaches, identifying weak assumptions, and raising the overall bar of rigor.

• Communicate findings through clear, structured narratives suitable for product, engineering, and leadership stakeholders.

• Mentor junior data scientists on modeling discipline, experimentation, and analytical reasoning.

• Strong theoretical foundation in:

  • Probability and statistics

  • Machine learning

  • Optimization or statistical decision-making

• 3+ years of experience as a Data Scientist, with demonstrated ownership over high-impact analytical or modeling problems.

• Proficiency in Python for data science and modeling (NumPy, Pandas, scikit-learn; PyTorch/TensorFlow as needed).

• Strong SQL skills and experience working with large-scale analytical datasets.

• Proven experience designing, executing, and analyzing controlled experiments in production environments.

• Ability to reason rigorously under uncertainty and produce quantitatively defensible recommendations.

• Strong communication skills, capable of explaining complex analytical results to non-technical audiences.

• Experience or strong interest in geospatial data, mobility data, or location-based systems.

• Familiarity with concepts such as GPS error modeling, time-series filtering, map-matching, or anomaly detection.

• Awareness of system and deployment constraints (latency, throughput, reliability) when moving models into production.

• Experience collaborating closely with backend teams; Golang familiarity is a plus, not a requirement.

• Experience working in Kubernetes-based platforms (e.g., OKD).

• Familiarity with deploying, monitoring, and maintaining ML or analytical models in production.

• Background in signal processing or large-scale real-time systems.

Help us shape the future of ride-hailing and urban mobility. Submit your CV and let’s build smarter cities together.