Senior Data Scientist Operations & Financial Modelling

Job Title: Senior Data Scientist Operations & Financial Modelling

Location: Bangalore, India

The Ceres Commitment

Ceres Environmental Services is a national leader in crisis management, rapid response, and disaster recovery. Since 1976, Ceres has been awarded over $3.2 billion in government-funded contracts, helping communities rebuild stronger and safer after disasters.

Our services span emergency response, environmental services, planning, and consulting including debris removal, blue roof installation, logistics, demolition, recycling, forest management, and coastal and marine restoration. As a licensed general contractor, Ceres brings unmatched expertise and execution excellence to every recovery effort.

Driven by Purpose, Powered by People

At Ceres, you’re more than an employee you’re part of a mission-driven team restoring hope and rebuilding lives. Every role, whether in leadership, field operations, or support functions, plays a critical part in helping communities recover when it matters most.

We value initiative, collaboration, diversity, and accountability. Our culture is built on resilience, teamwork, and empowerment, offering competitive compensation, global exposure, and opportunities to lead with impact.

If you act with compassion, urgency, and ownership you’ll thrive at Ceres.

Role Overview

We are seeking an experienced Data Scientist – Operations & Financial Modelling to drive data-driven decision-making across bidding, operations, and financial forecasting.

In this role, you will leverage advanced analytics, predictive modelling, and optimization techniques to help Ceres leadership optimize bid pricing, operational productivity, and margin outcomes by analysing large volumes of historical, operational, and geospatial data.

This is a hands-on, high-impact role working closely with U.S.-based stakeholders in pricing strategy, operational forecasting, and enterprise performance analytics.

Key Analytical Focus Areas

• Bid Win Models: Develop predictive models to estimate win probability by client, region, scope, contract type, and competitor profile.
• Pricing Engine: Build elasticity-based pricing models to support competitive and data-driven bid decisions.
• Operations Forecasting: Model production rates based on debris type, weather conditions, crew mix, equipment availability, and logistics routes.
• Margin Forecasting: Create financial simulation and scenario models to assess cost drivers, risk factors, and margin sensitivity.
• MLOps Foundation: Implement automated retraining, monitoring, validation, and governance frameworks for deployed models.

Key Responsibilities

Data Modelling & Analytics

• Design and develop scalable data models, pipelines, and predictive systems using Python and SQL across multiple data sources (Salesforce, ERP, GIS, weather, and operational systems).
• Perform feature engineering on large-scale operational, financial, and geospatial datasets.

Model Development & Deployment

• Build, validate, and deploy models including:

– Classification models (win/loss prediction)
– Regression models (price, cost, and margin forecasting)
– Time-series models (throughput, demand, and productivity projections)
– Optimization models (crew deployment, equipment routing, shift planning)

• Evaluate models using appropriate metrics such as AUC, RMSE, MAE, and uplift.
• Deploy model outputs into dashboards and decision-support tools (Power BI, Salesforce, Google Sheets, or internal applications).

Stakeholder Engagement & Insights

• Translate complex analytical outputs into clear, actionable insights and executive-ready narratives.
• Partner closely with U.S. business leaders, operations, finance, and strategy teams to support decision-making.

Governance & MLOps

• Ensure reproducibility, auditability, and version control across models and pipelines.
• Support model lifecycle management through MLOps best practices, monitoring, and performance tracking.

Required Qualifications

Education

• Postgraduate degree in Data Science, Statistics, Applied Mathematics, Engineering, Operations Research, or related fields.
• Graduates from IITs, NITs, IIITs, ISI, or other Tier-1 universities preferred.

Experience

• 6–10 years of hands-on data science experience in pricing, forecasting, operations analytics, or financial modelling.

Technical Skills

• Strong proficiency in Python (pandas, scikit-learn, statsmodels, XGBoost, LightGBM) and SQL.
• Experience building and maintaining data pipelines using tools such as Airflow and DBT.
• Exposure to geospatial analytics (GeoPandas, ArcGIS, QGIS) and weather datasets.
• Solid understanding of A/B testing, causal inference, and experimental design.
• Familiarity with MLOps frameworks for model deployment, versioning, and monitoring.

Soft Skills

• Strong communication, storytelling, and stakeholder management capabilities.
• Ability to work independently in a fast-paced, global environment.

What to Expect

You will work in a dynamic, data-driven environment, collaborating closely with U.S.-based stakeholders on real-world analytics problems that directly influence pricing and operational strategy.

Expect exposure to large-scale operational data, rapid prototyping of advanced models, and the opportunity to own analytics products end-to-end — from concept and design to deployment and adoption.

Why Join Ceres India?

At Ceres India, you won’t just have a job — you’ll have a mission. Your work directly impacts how quickly and effectively communities recover after disasters.