About the Role
We are looking for a highly motivated Data Scientist I to join our team. This role focuses on building and improving machine learning models that power real-time delivery time predictions at scale. The role involves designing robust regression and tree-based models, developing high-quality features from real-time and historical data, and contributing to systems that operate under strict latency and scale constraints.
You will collaborate closely with Data Engineers, MLOps Engineers, Product, and Operations teams to build scalable data pipelines, deploy models into production, and continuously improve prediction accuracy through monitoring and retraining.
The ideal candidate is passionate about solving real-world problems using data, has a strong foundation in machine learning and statistics, and is excited to work on large-scale systems where model performance directly impacts customer experience.
Responsibilities
- Perform advanced feature engineering using real-time and historical data (e.g. spatial signals, environmental factors)
- Design and train regression and tree-based models for high-scale, low-latency production systems
- Work on feature selection, model tuning, and validation to improve prediction accuracy and robustness
- Collaborate with data engineering teams to build scalable data pipelines
- Contribute to real-time inference systems
- Monitor model performance, detect drift, and support automated retraining pipelines
- Analyze model errors and build correction layers to capture residual patterns and edge cases
- Partner with product and operations teams to translate business problems into ML solutions
Requirements
Technical Skills (Must Have)
- Strong programming skills in Python and Strong SQL skills for data extraction, transformation, and analysis
- Solid understanding of machine learning algorithms, especially: Tree-based models (XGBoost, LightGBM, Random Forest)
- Regression techniques (linear, quantile, regularized models)
- Hands-on experience with feature engineering and feature selection techniques
- Understanding of model evaluation metrics and validation strategies
- Basic knowledge of model monitoring concepts
Core Competencies
- Strong analytical and problem-solving skills with attention to detail
- Ability to break down complex real-world problems into structured ML components
- Curiosity to explore non-obvious features and signals from data
- Strong debugging and data validation skills
- Ability to work in high-scale, fast-paced environments with evolving data
- Clear communication and collaboration with cross-functional teams
- Ownership mindset with a focus on impact and continuous improvement
Preferred Experience
- Experience working on time prediction / forecasting problems (e.g., ETA, demand forecasting)
- Exposure to large-scale ML systems or real-time prediction pipelines
- Experience with PySpark and Databricks
- Familiarity with spatial data processing (e.g., geo-coordinates, H3 indexing)
- Understanding of model monitoring, drift detection, and retraining pipelines
- Experience with A/B testing and experimentation frameworks
- Knowledge of handling high-throughput, low-latency ML systems
- 0–2 years of experience in Data Science, Machine Learning, or related roles
- Freshers with strong internships / academic projects in ML, data analysis, or large-scale data processing are encouraged to apply
- Hands-on exposure to building and evaluating ML models
- Experience working with real-world datasets, feature engineering, and model evaluation is a plus
Qualification
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field
- Strong foundation in probability, statistics, linear algebra, and machine learning fundamentals
- Demonstrated academic or project experience in applied machine learning and data-driven problem solving