Finance Manager, Data Science - Analytics

Expedia•Seattle, WA

59d•Hybrid

About The Position

Expedia Group brands power global travel for everyone, everywhere. We design cutting-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success. Why Join Us? To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win. We provide a full benefits package, including exciting travel perks, generous time-off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us. Finance Manager, Data Science - Analytics In this role, you will: Design, architect, and implement advanced artificial intelligence (AI) and machine learning (ML) solutions to address complex business challenges in analytics, forecasting, customer insights, and operational optimization. Apply predictive modeling, regression modeling, natural language processing (NLP), text analytics, and statistical algorithms to analyze diverse datasets, including transactional, operational, and unstructured text data. Manage the full AI/ML lifecycle from defining problem statements and scoping projects to data acquisition, cleansing, feature engineering, algorithm selection, model training, hyperparameter tuning, validation, deployment, and ongoing monitoring. Develop and evaluate models using both modern AI/ML algorithms (NLP, Decision Tree, Random Forest, Gradient Boosting). Perform advanced data extraction and transformation from multiple sources to create high-quality datasets for model development. Utilize big data platforms (Hadoop, Spark, Hive) and cloud services (Azure, GCP) for scalable AI/ML processing and deployment. Design and conduct A/B testing, multivariate testing, and other experimental frameworks to measure model performance and inform business strategy. Create and deliver compelling visualizations and dashboards using Tableau, Matplotlib, and Seaborn to present AI/ML results to technical and non-technical audiences. Apply explainable AI (XAI) methods to ensure transparency, interpretability, and fairness in model predictions. Collaborate with cross-functional teams, including product, engineering, finance, and operations, to integrate AI/ML models into business workflows, enhancing decision-making, operational efficiency, and customer engagement. Mentor junior team members on AI/ML best practices, NLP techniques, predictive modeling, and data science methodologies, fostering a culture of innovation and continuous improvement. This position is not eligible for relocation benefits or assistance. May work remotely or from home 2 days/week; must live within commuting distance of Expedia office.

Requirements

Master’s degree in Computer Science, Mathematics, Data Science, Physics, Engineering, or a related field and 5 years of experience in the job offered or in a data-related occupation.
Alternatively, a Bachelor’s degree in Computer Science, Mathematics, Data Science, Physics, Engineering, or a related field, followed by 7 years of progressive, post-baccalaureate experience in the job offered or in a data-related occupation.
Designing and applying AI/ML models and statistical methods using Python, R, SQL, and big data platforms.
Extracting and transforming data from various sources into structured datasets for advanced analytics and AI/ML applications.
Understanding of KPIs, business processes, and translating data into actionable insights.
Data visualization and storytelling through Tableau, Matplotlib, or Seaborn to communicate findings from AI/ML models.
Designing and executing A/B testing and experimental frameworks to evaluate model and business strategy performance.
Cloud-based AI/ML model deployment, monitoring, and data storage using platforms such as Azure and GCP.
Applying statistical concepts and methods to analyze and validate model outputs effectively.
Programming in Python or R for model development, data preprocessing, and analysis.
Knowledge of algorithms and frameworks for building predictive models, including NLP, Decision Tree, Random Forest, Gradient Boosting.
Cleaning and preparing datasets, including handling missing values, outliers, and performing feature engineering.
SQL and database systems to query, manage, and optimize datasets.
Natural Language Processing (NLP) and text analytics for classification, sentiment analysis, and pattern detection.
Big data frameworks such as Hadoop, Spark, and Hive for large-scale data processing.
Regression modeling for forecasting and trend analysis.

Responsibilities

Design, architect, and implement advanced artificial intelligence (AI) and machine learning (ML) solutions to address complex business challenges in analytics, forecasting, customer insights, and operational optimization.
Apply predictive modeling, regression modeling, natural language processing (NLP), text analytics, and statistical algorithms to analyze diverse datasets, including transactional, operational, and unstructured text data.
Manage the full AI/ML lifecycle from defining problem statements and scoping projects to data acquisition, cleansing, feature engineering, algorithm selection, model training, hyperparameter tuning, validation, deployment, and ongoing monitoring.
Develop and evaluate models using both modern AI/ML algorithms (NLP, Decision Tree, Random Forest, Gradient Boosting).
Perform advanced data extraction and transformation from multiple sources to create high-quality datasets for model development.
Utilize big data platforms (Hadoop, Spark, Hive) and cloud services (Azure, GCP) for scalable AI/ML processing and deployment.
Design and conduct A/B testing, multivariate testing, and other experimental frameworks to measure model performance and inform business strategy.
Create and deliver compelling visualizations and dashboards using Tableau, Matplotlib, and Seaborn to present AI/ML results to technical and non-technical audiences.
Apply explainable AI (XAI) methods to ensure transparency, interpretability, and fairness in model predictions.
Collaborate with cross-functional teams, including product, engineering, finance, and operations, to integrate AI/ML models into business workflows, enhancing decision-making, operational efficiency, and customer engagement.
Mentor junior team members on AI/ML best practices, NLP techniques, predictive modeling, and data science methodologies, fostering a culture of innovation and continuous improvement.