Sr Data Scientist - Enterprise Analytics

The Venetian Resort Las Vegas; Las Vegas (NV); On-site; USD 140,000 - 190,000; Full time

Job summary

Venetian Las Vegas Gaming, LLC is seeking a Sr Data Scientist to lead complex analytics that directly support enterprise decisions. You will manage the full lifecycle of data science projects, from problem definition through deployment and monitoring, collaborating with Operations, Marketing, Finance, and IT leadership to turn insights into actions.

The role requires mastering advanced ML, time-series forecasting, and robust governance practices, with 5% travel for conferences.

Qualifications

• 3 years of experience in data science or related roles.
• Proficiency with time series forecasting and ML model development.
• Experience with SQL, data exploration, and feature engineering.
• Familiarity with cloud platforms (GCP, AWS, Azure) and tools listed.
• Ability to communicate insights to non-technical stakeholders.

Responsibilities

• Lead complex analytical initiatives that support enterprise decision-making.
• Manage full lifecycle of data science projects from problem definition to deployment.
• Collaborate with cross-functional partners to gather requirements and translate analyses into recommendations.
• Provide thought leadership on modeling techniques and MLOps practices.
• Mentor other data scientists and ensure scalable, well-designed solutions.

Skills

Time series forecasting

Machine learning model deployment

EDA and feature engineering

SQL

Cloud platforms (GCP, Databricks, AWS,

Education

Master’s degree in Applied Data Science / Economics / Mathematics / Statistics

Tools

BigQuery

Vertex AI

Databricks

AWS

Azure

Job description

Position Overview

Full-time or part-time Full-time

Company Name

Venetian Las Vegas Gaming, LLC

Job title

Sr Data Scientist - Enterprise Analytics

Job Location

Las Vegas, Nevada

Position Overview

Responsible for leading complex analytical initiatives that directly support enterprise decision-making. Drive the full lifecycle of data science projects, including problem definition, data exploration, model development, validation, deployment, and ongoing performance monitoring.

Job Duties

Responsible for leading complex analytical initiatives that directly support enterprise decision-making. Drive the full lifecycle of data science projects, including problem definition, data exploration, model development, validation, deployment, and ongoing performance monitoring. Take broad or ambiguous business questions and transform them into clear analytical approaches that produce measurable impact. Collaborate closely with cross-functional partners across Operations, Marketing, Finance, Revenue Management, IT, and leadership, including gathering requirements, identifying opportunities for automation and optimization, and translating analytical outcomes into recommendations that can be understood and acted upon by both technical and nontechnical audiences. Serve as a technical expert within the team, providing thought leadership on emerging modeling techniques, advanced machine learning and AI methodologies, best practices for experimentation and validation, and modern approaches to model governance and MLOps. Guide and mentor other data scientists, elevate the overall technical rigor of the team, and ensure that solutions are scalable, well-designed, and aligned with long-term business strategy. Introduce innovative techniques, strengthen the team’s analytical capabilities, and support the organization in making high-quality, data-driven decisions. 5% domestic travel for work-related conferences.

Minimum Education & Experience Requirements

Master’s degree or foreign equivalent in Applied Data Science, Economics, Mathematics, Statistics or closely related field of study, plus 3 years of experience in job offered or as a Data Scientist.

Requires 3 years of experience in

• time series forecasting techniques (including ARIMA, Prophet) while working with complex, high-volume datasets;
• building, deploying, and tuning machine learning models (Random Forest, XGBoost) and deep learning models (CNN, RNN, LSTMs, Transformers) for structured and unstructured data;
• exploratory data analysis (EDA), feature engineering, and statistical modeling to extract actionable insights;
• SQL for querying databases and datasets;
• cloud platforms (including GCP, Databricks, Microsoft Azure, AWS) and using GCP tools such as BigQuery, BQ/ML, Vertex AI, and Cloud Functions for data storage, processing, and model development;
• Python and machine learning libraries (TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost);
• data visualization tools (Matplotlib, Seaborn) to communicate complex insights to non-technical stakeholders.