Manager, Data Science at AmaWaterways, LLC

Calabasas, California, United States -

Full Time

Start Date

Immediate

Expiry Date

16 May, 26

Salary

165000.0

Posted On

15 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Data Warehouse Design, Strategy, Architecture, Analytics, Team Building, Data Governance, Cloud DW Implementation, BI Tools, ML Stack, Orchestration, Data Quality, Forecasting Models, Segmentation Models, Optimization Models, Stakeholder Communication

Industry

Travel Arrangements

Description

At AmaWaterways, we believe meaningful careers begin with purpose, passion and a shared commitment to delivering unforgettable experiences. For those who value curiosity, connection and personal enrichment, AmaWaterways offers the opportunity to help craft meaningful river journeys that invite travelers to follow their own current. Built on a foundation of heartfelt hospitality, we treat our guests—and each other—with genuine care, warmth and respect. AmaWaterways fosters a collaborative environment both onboard our ships and across our global network of offices, where team members grow together, support one another and take pride in upholding the high standards and thoughtful service our company is known for. We invite talented, motivated professionals to explore our career opportunities and begin their journey with AmaWaterways today. SUMMARY: The Manager, Data Science role is responsible for designing and building the data science capability from scratch, partnering closely with Engineering and IT to stand up a modern data warehouse, and translating business problems into scalable, data-driven solutions. This is a hands-on role that blends strategy, architecture, analytics, and team building. ESSENTIAL DUTIES AND RESPONSIBILITIES: Core duties and responsibilities include the following. Other duties may be assigned. * Define the vision, roadmap, and operating model for the data science function * Partner with Engineering/IT to design and implement the company’s first enterprise data warehouse * Establish data standards, governance, documentation, and best practices * Work with stakeholders to identify data sources and prioritize ingestion into the warehouse * Help select and implement core tools (cloud DW, BI, ML stack, orchestration, etc.) * Ensure data quality, reliability, and scalability from day one * Lead the development of initial analytical models, dashboards, and use cases * Translate business questions into analytical frameworks and data science solutions * Develop forecasting, segmentation, and optimization models for revenue management * Partner with Product, Marketing, Finance, Operations, and Commercial teams to identify high-impact use cases * Lead cross-functional data initiatives and evangelize data-driven decision making * Hire, mentor, and develop the company’s first data scientists and analysts * Define roles, career paths, and performance expectations * Foster a culture of experimentation, rigor, and collaboration OTHER QUALIFICATIONS: * Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, or related field; Master’s or PhD preferred * 5-7+ years of experience in analytics, data science, or applied ML * 3–5+ years of people management and team-building experience * Proven experience designing or contributing to a modern data warehouse or data platform * Strong hands-on experience with Python/R, SQL, and cloud data environments * Experience with ML frameworks and productionizing models (e.g., scikit-learn, TensorFlow, PyTorch) * Strong understanding of data engineering concepts (ETL/ELT, orchestration, version control, CI/CD) * Excellent business acumen and stakeholder communication skills * Experience in Snowflake and Salesforce products is a plus

Responsibilities

This role is responsible for designing and building the data science capability from the ground up, collaborating with Engineering and IT to establish a modern data warehouse. Key duties involve defining the function's vision, implementing the enterprise data warehouse, establishing data standards, and leading the development of initial analytical models and use cases.