Tour De East Coast ride share app - NS-030

Genre de projet: Innovation
Discipline(s) souhaitée(s): Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques
Entreprise: Tour De East Coast
Durée du projet: Flexible
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): Lower Sackville, NS, Canada
Nombre de postes: 1
Niveau de scolarité désiré: CollègeÉtudes de premier cycle/baccalauréatMaîtriseDoctoratRecherche postdoctoraleNouvelle diplômée/nouveau diplômé
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: No

Au sujet de l’entreprise: 

Tour De East Coast stands as a trailblazer in the realm of shared mobility, offering a wide spectrum of active transportation solutions tailored to meet the evolving needs of modern communities. With a strong emphasis on versatility, the company serves a diverse clientele—including cities, universities, resorts, and businesses—by integrating sustainable mobility options into their daily operations.
As the shared mobility market continues to expand, particularly in areas like e-hailing and micro-mobility, Tour De East Coast plays a vital role in shaping the future of transportation. Our services go beyond convenience; we foster healthier lifestyles, reduce environmental impact, and promote smarter urban planning. Whether through bike-sharing programs, electric scooters, or other innovative platforms, our company is committed to building ecosystems that support long-term sustainability and connectivity.
Tour De East Coast’s approach reflects a deep understanding of the shifting transportation landscape. By anticipating future trends and responding to current demands, we position ourselves not just as a service provider, but as a catalyst for change. Our work helps redefine how people move through cities and campuses, making mobility more inclusive, efficient, and environmentally responsible.

Veuillez décrire le projet.: 

Integrating a real-time AI assistant within our ride-share platform, we will introduce both incremental and disruptive innovations that transform business models, product capabilities, and process efficiencies. On the business model front, the assistant supports a freemium structure whereby basic real-time route optimization and user engagement remain free while advanced predictive analytics, demand forecasting, and hyper-personalized promotions become premium offerings, unlocking new recurring revenue streams and incentivizing platform loyalty. From a product development standpoint, delivering an embedded recommendation engine for fleet operators and a conversational interface for riders enhances core app functionality. Operators will receive dynamic dispatch suggestions based on historical trip data, real-time traffic flow, supply–demand imbalances, and driver performance metrics. Riders will engage through context-aware chat interactions that handle booking inquiries, provide ETA updates, suggest multi-leg routes, and deliver personalized incentives based on user behavior and preferences. Process improvements include automating hours traditionally spent in manual dispatch coordination and customer support, leveraging reinforcement learning to continuously refine routing policies for reduced idle time and improved service levels. The candidate will perform stakeholder analysis, define key performance indicators such as average waiting time reduction and engagement rates, design ETL pipelines for streaming GPS, trip, and user-behavior data, and build machine-learning models for route recommendation and intent-aware dialogue. Integration tasks involve containerizing microservices, exposing RESTful APIs for real-time inference, conducting A/B tests to validate business impact, and instrumenting monitoring dashboards for latency, accuracy, and user satisfaction metrics. Methodology includes data preprocessing with feature engineering on temporal, spatial, and environmental variables; collaborative filtering and contextual bandits for adaptive personalization; transformer-based architectures for natural-language understanding; and MLOps best practices with automated model retraining, version control, and CI/CD pipelines. Together, these efforts will culminate in an AI assistant that streamlines operations, captivates users with tailored experiences, and drives growth through intelligent automation and personalization.

Expertise ou compétences exigées: 

Candidates must have advanced proficiency in Python, SQL, and a strong command of real-time streaming frameworks such as Apache Kafka and Spark Streaming. Experience with containerization and orchestration using Docker and Kubernetes is essential, along with development familiarity with cloud ML services (AWS SageMaker, GCP AI Platform, or Azure ML). They should be proficient in machine-learning libraries including TensorFlow, PyTorch, and scikit-learn, and have practical expertise with NLP tools like Hugging Face Transformers or Rasa. Data pipeline orchestration skills with Apache Airflow, ETL best practices, and database management (PostgreSQL, MongoDB, Redis) are required. Competence in API design (RESTful, GraphQL), MLOps toolchains (MLflow, DVC, CI/CD with Jenkins or GitLab CI), and monitoring solutions (Prometheus, Grafana) is also critical. Familiarity with agile methodologies and version control via Git completes the profile