We are seeking an experienced Data Scientist to join our team to develop, optimize, and enhance production-grade machine learning solutions predicting critical operational milestonessuch as Estimated Start/Boarding Times and Estimated Departure/Completion Timesfor real-time, customer-facing tracking and journey platforms.

This role requires deep expertise in time-based predictive modeling, scalable data processing within the Databricks ecosystem, and practical familiarity with end-to-end MLOps lifecycle practices.

Key Responsibilities :

- Predictive Modeling & Forecasting :

i. Design, develop, validate, and optimize machine learning models for key operational milestones and departure/event-time prediction.

ii. Evaluate regression, gradient-boosting, ensemble, and time-aware modeling approaches based on operational and data requirements.

iii. Build models resilient to dynamic operating conditions, irregular operational events, and shifting data distributions.

- Uncertainty Estimation & Benchmarking :

i. Estimate prediction confidence and uncertainty, defining fallback logic for customer-facing prediction stability.

ii. Continuously benchmark model performance against baselines and operational ground-truth reference timestamps.

- Data Analysis & Feature Engineering :

i. Analyze historical and near-real-time operational data to uncover factors driving operational timelinesincluding scheduled versus actual milestone progress, delay history, operational status updates, resource/asset allocation, congestion, environmental/external constraints, and movement signals.

ii. Build reusable feature pipelines and data quality validations.

- Databricks Workflows & Scalable Processing :

i. Develop scalable data preparation, experimentation, training, and scoring workflows inside Databricks utilizing PySpark, Delta Lake, SQL, and Databricks Workflows.

ii. Ensure modular, parameterized, reproducible code in close alignment with Data Engineering.

- MLOps & Model Lifecycle Management :

i. Leverage MLflow for experiment tracking, model comparison, artifact management, and Model Registry governance.

ii. Define metrics for accuracy, inference latency, data quality, data drift, and model degradation.

iii. Recommend retraining triggers, validation gates, rollback criteria, and handover specifications for DevOps deployment.

- Cross-Functional Collaboration & Delivery :

i. Partner with Product, Data Engineering, DevOps, and Operations stakeholders to translate business goals into measurable modeling objectives.

ii. Support UAT, production rollout, performance monitoring, and deliver thorough technical documentation.

Required Qualifications :

- Professional Experience : 4+ years of hands-on professional experience in Data Science and Machine Learning, with a track record of developing and delivering production-grade ML models using large structured and semi-structured datasets.

- Predictive & Time-Based Modeling : Demonstrated expertise in predictive analytics, time-based prediction, forecasting, ETA/ETD estimation, milestone forecasting, or real-time event-time prediction.

- Databricks Ecosystem : Hands-on experience developing within Databricks, utilizing notebooks, Delta Lake, Databricks Workflows, and Spark-based processing (PySpark).

- Unity Catalog exposure is preferred.

- MLOps Lifecycle Awareness : Practical experience with MLflow (experiment tracking, model registry, artefact versioning), reproducible pipeline design, CI/CD concepts, drift detection, and automated retraining/rollback workflows.

- Programming & Core Languages : Advanced proficiency in Python, SQL, and PySpark for scalable data manipulation, feature engineering, and statistical analysis.

- Evaluation & Validation : Solid grounding in evaluation metrics including MAE, RMSE, percentile errors, threshold-based accuracy, calibration, segment-level validation, and temporal back-testing.

- Cross-Functional Collaboration : Proven ability to work in Agile environments collaborating closely with Data Engineering, DevOps, and business stakeholders.

- Education : Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Operations Research, Artificial Intelligence, or a related quantitative field.

Preferred Qualifications :

- Domain Experience : Prior experience in transportation, mobility, logistics, supply chain, or similar real-time event-time and milestone prediction systems.

- High-Reliability Customer Products : Experience delivering customer-facing prediction products with strict requirements for low latency, high stability, and explainability.

- Cloud & Lakehouse Architecture : Experience working across enterprise cloud environments and modern lakehouse data architectures