Pricing and Demand Simulator Development - ON-1143

Genre de projet: Innovation
Discipline(s) souhaitée(s): Statistiques / études actuarielles, Sciences mathématiques, Science économique, Sciences sociales et humaines
Entreprise: eXalt Solutions
Durée du projet: 4 à 6 mois
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): Toronto, ON, Canada
Nombre de postes: 1
Niveau de scolarité désiré: MaîtriseDoctorat
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: No

Au sujet de l’entreprise: 

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.

Veuillez décrire le projet.: 

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

Expertise ou compétences exigées: 

• 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.