Pricing and Demand Simulator Development - ON-1143

Project type: Innovation
Desired discipline(s): Statistics / Actuarial sciences, Mathematical Sciences, Economics, Social Sciences & Humanities
Company: eXalt Solutions
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Toronto, ON, Canada
No. of positions: 1
Desired education level: Master'sPhD
Open to applicants registered at an institution outside of Canada: No

About the company: 

eXalt Solutions is redefining the future of B2B sales through AI.

Our no-code Knowledge Work as a Service (KWaaS) platform empowers companies to build Knowledge Bots that act as Advisors, Analysts, and Administrators—capturing expertise and automating decision-making at scale.

By turning institutional know-how into intelligent workflows, eXalt Solutions helps leading technology brands accelerate sales, reduce complexity, and grow faster. With AI-powered precision and a codeless foundation, we’re transforming how organizations sell, collaborate, and scale knowledge across their partner ecosystems.

Describe the project.: 

Design and implement statistical models (time series, regression, elasticity models) to capture demand patterns and relationships dynamically using source data fed via pipelines and APIs:

- Develop and train machine learning models (neural networks, gradient boosting, ensemble methods) to enhance prediction accuracy
- Create a hybrid modeling framework that intelligently combines statistical and AI approaches to leverage the strengths of both
- Work with large-scale datasets to extract features, identify patterns, and validate model performance
- Build simulation engines that can test "what-if" scenarios across multiple demand drivers (price, promotions, seasonality, external factors)
- Collaborate with business stakeholders to understand requirements and translate them into technical specifications
- Conduct rigorous model validation, back testing, and sensitivity analysis
- Optimize model performance for accuracy, interpretability, and computational efficiency
- Document methodologies, assumptions, and model limitations clearly

Required expertise/skills: 

• Master's or PhD in Statistics, Data Science, Economics, Marketing, Computer Science, or related quantitative field
• Experience building forecasting or predictive models in production environments
• Strong proficiency in statistical modeling techniques (ARIMA, regression, GLMs, Bayesian methods)
• Hands-on experience with modern ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost)
• Expert-level programming skills in Python
• Experience working with time series data and demand forecasting
• In depth knowledge of preference modeling techniques including conjoint analysis
• Expertise in pricing, promotions and product research
• Solid understanding of model evaluation metrics and validation techniques
• Ability to communicate complex technical concepts to non-technical stakeholders.