Data Scientist

• Operations Forecasting : Model production rates by debris type, weather, crew mix, and logistics routes.
• Evaluate models using statistical and business metrics (AUC, RMSE, MAE, uplift).
• Conduct feature engineering on operational and geospatial datasets (e.g., buffer zones, competitor features, crew metrics).
• Collaborate with U.S. leadership on strategic bids and cost-optimization projects.
• Margin Forecast : Create financial simulation models to assess cost drivers (fuel, disposal, distance, etc.) and scenario outcomes.

This is a hands-on, high-impact role interfacing with global stakeholders, particularly in pricing strategy, operational forecasting, and business performance analytics.

• Bid Win Model : Develop predictive models estimating win probability by client, region, scope, and competitor profile.
• Deploy models into dashboards or applications (e.g., Power BI, Salesforce, Google Sheets).

You will leverage advanced analytics and predictive modeling to help Ceres management optimize bid pricing, operational productivity, and margin forecasting by analyzing large volumes of historical and operational data.

• Design and develop data models, pipelines, and predictive systems using Python and SQL across multiple data sources Salesforce, ERP (NetSuite/Vista/ERPLY), GIS, and weather data.
• MLOps Foundation : Implement automated retraining, monitoring, and governance frameworks for deployed models.
• Pricing Engine : Build elasticity-based pricing guidance to support competitive bid decisions.
• Maintain reproducibility and data governance, ensuring version control and auditability.

• Experience working with U.S. clients or in global delivery centers supporting analytics for large enterprises.
• 610+ years of experience in applied data science within pricing, demand forecasting, operations, or financial modeling.
• Experience integrating financial modeling with operations and pricing analytics.
• Proficiency in Python (pandas, scikit-learn, statsmodels, XGBoost/LightGBM) and SQL.
• Understanding of supply chain, equipment utilization, and manpower productivity analytics.

We are seeking an experienced Data Scientist to drive data-driven decision-making for our bids, operations, and financial modeling functions.

• Prior work in construction, infrastructure, logistics, utilities, or disaster management sectors.
• Hands-on experience in building data pipelines (Airflow, DBT) and working with structured/unstructured data.
• Present actionable insights and executive-level narratives to senior management.
• Exposure to geospatial analytics (GeoPandas, ArcGIS, QGIS), weather data, or location intelligence.
• Familiarity with MLOps frameworks for versioning, monitoring, and automated retraining.
• Understanding of A/B testing, causal inference, and statistical experiment design.
• Postgraduate degree in Data Science, Statistics, Applied Mathematics, Engineering, Operations Research, or related fields (IITs, NITs, IIITs, ISI, or Tier-1 Universities preferred).
• Work in a data-first culture where analytics directly influence pricing, operations, and profitability.
• Excellent communication skills to translate analytical insights into business impact.