ML/AI Engineer – Smart Pricing

Department: Data Science / Revenue Management / Engineering Reports To: Head of Data Science or Chief Data Officer About Swyftbooking Swyftbooking is a fast-growing travel technology platform powering seamless booking experiences across flights, hotels, car rentals, and private rentals. We aim to maximize occupancy, optimize yield, and deliver competitive pricing through intelligent, data-driven decision-making. We are building a dedicated Smart Pricing Guild to develop and operationalize machine learning models that dynamically adjust pricing, promotions, and inventory controls across all modules. Role Overview As a Smart Pricing ML/AI Engineer, you will own the end-to-end lifecycle of pricing models and recommendations across Swyftbooking modules (hotels, cars, private rentals, and experiences). You will partner with Revenue Management, Product, Data Platform, and Engineering to design, implement, validate, deploy, and monitor ML-driven pricing strategies that improve revenue, utilization, and customer value while maintaining fairness and compliance. Key Responsibilities Design, develop, and productionize machine learning models for dynamic pricing, including demand forecasting, price elasticity, inventory optimization, discount/promotion strategies, and competitive benchmarking. Build end-to-end pricing workflows: data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining pipelines (MLOps). Collaborate with Product and Business teams to translate pricing hypotheses into measurable experiments and KPIs (GTV, ADR, occupancy, conversion, churn, CAC). Implement real-time or near-real-time pricing decisions where applicable, including rate parity, inventory constraints, and surge pricing logic. Develop and maintain data pipelines to aggregate internal data (bookings, views, searches, inventory) and external signals (seasonality, events, competition) for pricing models. Create robust evaluation frameworks (A/B/n testing, causal inference, backtesting) and establish governance for model risk and fairness. Integrate pricing outputs with Swyftbooking's APIs and microservices, ensuring low latency, fault tolerance, and traceability. Implement telemetry, monitoring, and alerting for model performance, data quality, drift detection, and pricing anomalies. Collaborate on data privacy and security, ensuring compliant handling of user data and pricing information. Document methodologies, model decisions, and assumptions; contribute to a living pricing knowledge base. Stay current with ML/AI best practices, pricing theory, and travel industry dynamics; propose innovations and roadmap items. Required Qualifications 4+ years of professional experience in machine learning, data science, or AI engineering, with a track record of shipping product-facing ML solutions. Strong experience in pricing, revenue management, demand forecasting, or dynamic pricing in e-commerce/travel, marketplaces, or similar domains. Proficiency in Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow) and SQL; experience with data notebooks and experimentation tooling. Experience with ML model development lifecycle: feature engineering, model training, evaluation, deployment, and monitoring (MLOps practices). Knowledge of time-series forecasting (ARIMA, Prophet, LSTM/GRU, Prophet, etc.), regression, and optimization techniques. Familiarity with recommender or pricing optimization frameworks and constraint programming. Experience deploying models in production (cloud platforms like AWS/GCP/Azure), containerization (Docker), and orchestration (Kubernetes). Strong analytical mindset with the ability to translate business metrics into technical experiments; excellent problem-solving skills. Collaboration and communication skills across data science, engineering, product, and leadership stakeholders. Familiarity with data governance, privacy, and security considerations in ML. Nice-to-Haves Experience with multi-objective optimization (revenue, occupancy, customer satisfaction) and Pareto efficiency concepts. Knowledge of reinforcement learning or bandit algorithms for pricing exploration-exploitation. Knowledge of pricing strategies in specific Swyftbooking modules (hotels, car rentals, private rentals) and ability to tailor models per domain. Experience with feature stores, data lineage, and model versioning (MLflow, SageMaker, Kubeflow, or equivalent). Familiarity with experimentation platforms and statistical significance testing (A/B testing design, bootstrap methods). Experience with GDS/OTA pricing dynamics or fare/availability optimization. Soft Skills Strategic thinker with business acumen and a bias for action. Strong communicator who can explain complex ML concepts to non-technical stakeholders. Detail-oriented with rigorous experimentation discipline. Collaborative, with ability to work in cross-functional teams and influence without authority.