This role demands a blend of logistics modelling expertise, simulation capabilities, BI proficiency, and analytical storytelling. You will transform customer datasets into actionable simulations, dashboards, and ROI models that prove the company's ability to model real-world logistics networks with depth and accuracy.

Key Responsibilities :

1. Simulation & Logistics Modelling

- Build high-fidelity simulations for customer logistics environments, including FTL / LTL routing, Last-mile delivery, Store fulfillment, Procurement and multi-pick flows and Multi-day and multi-leg planning

- Configure fleet rules, constraints, capacities, service-time norms, geo-fences, and appointment windows into models.

- Create baseline vs optimized comparisons across cost, time, distance, utilization, and SLA performance.

2. Analytics, BI & Insight Generation

- Build BI dashboards and analytical views using tools such as Power BI, Tableau, Qlik, or similar.

- Present trends, bottlenecks, simulation outputs, and optimization deltas through structured dashboards.

- Develop KPI packs covering fleet utilization, route density, delivery productivity, costdistance curves, time-window adherence, and driver/vehicle performance.

- Convert simulation data into executive-level insights and ROI narratives.

- Build sensitivity analyses, cost scenarios, and what-if comparisons.

3. Pre-Sales, POCs & Customer Workshops

- Own modelling configuration and analytical support for POCs, trials, and scenario-based evaluations.

- Support workshops with BI-backed visualizations and modelling insights.

- Prepare simulation output decks, dashboards, and modelling summaries for CXOs, Operations Heads, and Digital Transformation teams.

4. Technical Solution Design

- Translate customer operations into data and modelling constructs such as vehicle rules, load structures, SKU constraints, rate cards, shift patterns, and operating windows.

- Collaborate with Solution Managers and Product teams to ensure simulations align with real-world operations.

- Build reusable modelling templates and analytical frameworks across industries.

5. Data Preparation & Engineering

- Clean, transform, and structure customer datasets for modelling.

- Create delivery matrices, distancetime analyses, cluster density maps, and load utilization patterns.

- Develop modelling pipelines that reduce preparation time for future engagements.

6. Continuous Improvement & Knowledge Development

- Maintain strong knowledge of routing engines, heuristics/meta-heuristics, and optimization modelling.

- Create libraries of dashboards, BI templates, simulation playbooks, and reusable assets.

- Feed modelling learnings into the product roadmap and internal methodologies.

Minimum Qualifications

1. Minimum 3+ years of experience in logistics modelling, supply chain analytics, BI, simulation engineering, or solutions engineering.

2. Prior experience with logistics or supply chain planning tools is highly preferred.

3. Strong Excel, SQL, and Python skills for modelling and data manipulation.

4. Expertise in BI tools such as Power BI, Tableau, Qlik, Looker, or similar.

5. Understanding of heuristics and optimization concepts (LP/MIP, routing engines).

6. Ability to run iterative simulations and analyse outputs in depth.

7. Solid understanding of logistics operations including FTL/LTL, last-mile, consolidation, routing, warehouse-to-store flows, rate cards, and driver/vehicle rules.

8. Exposure to multi-constraint network planning and scenario analysis.

9. Strong ability to translate analytical output into clear business narratives.

10. Comfortable presenting modelling insights to CXOs, operations leaders, and IT teams.