Architect, develop, and deploy end-to-end machine learning models to solve business problems in areas such as forecasting, recommendation, NLP, or computer vision.
Conduct in-depth exploratory data analysis (EDA), perform sophisticated feature engineering, and collaborate with data engineers to build robust data preprocessing pipelines.
Lead the prototyping and experimentation of novel algorithms, rigorously testing hypotheses and evaluating model performance against key business metrics.
Write clean, scalable, and production-ready code (primarily Python) for model training, inference, and integration via APIs.
Take full ownership of the ML model lifecycle, including versioning, monitoring for performance degradation or drift, and implementing automated retraining workflows.
Collaborate with MLOps and software engineers to seamlessly integrate ML models into our production applications and services.
Clearly articulate complex findings, model behaviors, and performance metrics to both technical and non-technical stakeholders.
Any ad hoc duties as assigned
Job Requirements:
Bachelor's or master's degree in computer science, Statistics, Mathematics, or a related quantitative field is preferred.
Minimum 5 years of relevant experiences preferred
Deep understanding of machine learning theory, algorithms (e.g., regression, classification, clustering), and statistical principles.
Expert proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, Matplotlib).
Extensive hands-on experience with at least one major deep learning framework, such as TensorFlow, PyTorch, or JAX.
Strong SQL skills and experience working with large-scale datasets from data warehouses or data lakes.
Proven experience in building and deploying ML models in a commercial environment using a major cloud platform (AWS, GCP, or Azure).
Solid software engineering fundamentals, including object-oriented design, version control (Git), testing, and writing maintainable code.
Familiarity with MLOps principles and tools (e.g., MLflow, DVC, Kubeflow) for experiment tracking and model management.
Experience in building solutions using Generative AI models and frameworks (e.g., LangChain, LlamaIndex) is a significant plus.
Direct experience in the ride-hailing industry and a strong understanding of dynamic pricing models would be a significant advantage.
Proactive and analytical approach to problem-solving, with a keen eye for detail.
Strong ownership mindset with the ability to work independently and drive projects from concept to production.
Excellent ability to translate complex technical concepts into clear business context for stakeholders.
