Description
Arcadis is the world's leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets.
We are more than 34,000 people, in over 30 countries, dedicated to improving quality of life. Everyone has an significant role to play. With the power of many curious minds, together we can solve the world’s most complex challenges and deliver more impact together.
Role description:
As a senior team member, you will lead end-to-end economic modelling
and forecasting for traffic due diligence assignments. You will develop modelling
frameworks, guide ETL and data architecture, oversee model governance and validation,
convert analytical findings into explicit commercial recommendations, and work directly with
clients and project leaders.
Role accountabilities:
• Lead and own investment-grade economic analysis and modelling for traffic due
diligence, tariff strategy, sensitivity and scenario analysis, and cost–benefit inputs.
• Lead development of toll revenue forecasting frameworks and long-term demand
models; ensure robust validation and defensible assumptions.
• Architect and oversee data ingestion, ETL pipelines and data quality processes for toll
MIS, OD surveys and auxiliary sources.
• Develop, validate and document advanced time-series, state-space and econometric
models; implement ML solutions where appropriate.
• Present findings and recommendations to senior clients, stakeholders and investment
committees; produce high-quality technical reports and executive summaries.
• Coordinate closely with traffic engineering teams for field data validation, survey design
and technical reconciliation.
• Mentor and review work of junior analysts; manage small project teams and timelines.
• Contribute to business development by scoping technical approaches, estimating effort
and advising on methodology for proposals.
Qualifications & Experience:
• Master’s degree in Economics, Econometrics, Transport Plann
📌 Transport Economist Cum Data Analyst Hyderabad
🏢 Arcadis
📍 Hyderabad
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