Role Purpose: The Data Scientist is responsible for architecting, developing, and deploying advanced AI and Machine Learning models to optimize express distribution networks. This role bridges the gap between complex data theory and operational excellence, identifying High impact use cases, such as route optimization, demand forecasting, and automated sorting to drive measurable business impact and efficiency.

Key Accountability Area :

Model Accuracy & Precision: Achieving pre-defined performance benchmarks (e.g., MAPE for forecasting, F1-score for classification) within 2026 operational standards.

Business Impact (ROI): Measurable cost savings or revenue growth (e.g., % reduction in "cost-per-kg," % improvement in Last Mile Delivery success).

Time-to-Production: Average lead time from ideation to the deployment of a robust, scalable model.

Model Uptime & Reliability: Ensuring deployed AI solutions maintain high availability and performance consistency with minimal drift.

Stakeholder Satisfaction: Qualitative feedback from business partners on the clarity of insights and solution utility.

Preferred: Certifications in Cloud Machine Learning (e.g., AWS Certified Data Research or Google Professional ML Engineer)

Total Experience: 5+years in Data Science/Analytics.





Relevant Experience: Minimum 4+ years of hands-on experience in building and deploying ML models in a production environment.

Industry Background: Prior experience in Logistics, Supply Chain, E-commerce, or Express Distribution is highly preferred.

Proven Track Record: Demonstrated experience leading at least 2 - 3 large-scale AI projects from ideation to measurable business delivery.

Technical / Functional Competencies:

Programming & Frameworks: Expert proficiency in Python or R; deep experience with ML libraries such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.

Advanced Analytics: Expertise in deep learning, natural language processing (NLP), and reinforcement learning (highly relevant for logistics routing).

Data Engineering Integration: Proficiency in SQL and experience working with Big Data technologies (Spark, Hadoop) and cloud platforms (AWS SageMaker, Azure ML, or Google Vertex AI).

MLOps: Robust understanding of model versioning (MLflow), containerization (Docker/Kubernetes), and CI/CD pipelines for ML.

Optimization Modeling: Experience with linear programming and combinatorial optimization for supply chain logistics.

📌 Data Scientist - Mumbai/ Hyderabad
🏢 Allcargo Logistics
📍 Hyderabad