Senior Data Scientist (Geo Search)

Geo Search runs the search that tens of millions of riders use to say where they are going, across many countries where the commercial maps everyone else leans on are often wrong, incomplete, or simply missing. Getting search right in those conditions is one of the hardest and highest-leverage problems at inDrive. The same search and location capabilities also power other verticals such as courier, cargo, and intercity. The team owns place search and autocomplete, geocoding, ranking and personalization, location detection and pickup selection, integrations with external providers such as Google and 2GIS, and its own search screens on Android and iOS. It is measured on order conversion rate, search conversion, successful search sessions, and mean reciprocal rank. We are looking for a Senior Data Scientist engineer who will design and build the machine learning systems that power search ranking and personalized recommendations at scale. The work spans deep learning model development, the use of large language models, and production deployment. You will measure your impact through offline evaluation metrics and the results of online A/B tests, which feed directly into user engagement and business outcomes. Key Responsibilities Design and build the deep learning systems behind search ranking, session-based recommendations, and multi-objective personalization, learning from rider behavior and serving users across markets Make ranking consume geographic context and own the recommendation and ranking of pickup points Translate business goals into ML objectives with non-functional requirements Lead evaluation end-to-end, from offline metrics to the design of online A/B tests, and prove a change improves engagement before it ships Partner with backend engineers to take models from prototype to production, including model serving and latency Partner with the product manager and operations to turn behavior analysis into concrete features and requirements Own the ML lifecycle in production, serving, monitoring for drift and building the retraining pipelines that hold quality as data shifts Skills, Knowledge and Expertise An academic background in a quantitative field such as Computer Science, Mathematics, or a related discipline will be a plus 5 or more years of machine learning engineering experience, to confirm against the Senior II level internally, with at least three of them building and deploying deep learning models in production Direct experience with search, NLP, ranking, recommendation, or relevance systems. Expert-level proficiency in Python and its core data science libraries and SQL (e.g., PySpark, Pandas, NumPy, Scikit-learn, PyTorch) The ability to design an ML system from scratch in at least one area, including data analysis, annotation, and processing through to a model serving in production Experience turning a business goal into an ML problem with the right proxy metrics and non functional requirements, and designing or substantially contributing to the A/B tests and statistical evaluation that prove impact on user behavior Experience using MLOps tools and practices to manage the ML model lifecycle Experience deploying models to production on ML serving infrastructure and optimizing for latency, and awareness of concept drift and how to detect and manage it The ability to influence teammates and partner teams, and to communicate complex results clearly Strong pluses Experience fine tuning and deploying large language models (or small language models), for query understanding or relevance Subject matter depth in geocoding or autocomplete relevance specifically Experience in mapping, location, or geospatial products Experience building for developing markets, where the underlying map and address data is weak Experience with BigQuery or Databricks certifications Subject matter depth in search, geocoding, ranking, or recommendation systems Experience in mapping, location, or geospatial products Conditions & Benefits Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.