Senior Data Scientist

The Senior Data Scientist plays a critical role in shaping the future of business by developing algorithms and models that forecast trends and translate business needs into actionable analytics for executive decision-making. With a strong foundation in AI/ML and pricing optimization, this role leads ML model experimentation — scaling sensitivity-to-elasticity experiments across more markets simultaneously — while driving model measurement, tuning, and pilot measurement strategy. By conducting large-scale experiments, the Senior Data Scientist addresses complex business questions and uncovers hidden relationships within extensive datasets.

Duties and Responsibilities

Perform Exploratory Data Analysis (EDA) and initial investigations on data to discover patterns, spot anomalies, and check assumptions

Build predictive models using statistical and machine learning techniques, with a focus on pricing optimization and elasticity modeling

Augment ML model experimentation by scaling sensitivity-to-elasticity analyses, enabling more experiments and markets to run concurrently

Design and execute pilot measurement strategies to evaluate model performance in live environments before full deployment

Continuously measure, monitor, tune, and optimize models within the production environment to ensure health, accuracy, and business impact

Create visual representations of data to communicate findings effectively and share with other Data Scientists for input and storytelling

Work with cross-functional teams, including engineers, service owners, product owners, product managers, and business stakeholders, to understand data needs and challenges

Prepare reports and presentations to convey insights and recommendations

Gather data from various sources and ensure its quality and integrity

Stay updated with the latest tools, techniques, and industry trends in data science and AI/ML and bring ideas to the table for how to improve the use of data and analytics

Design and execute comprehensive data analyses using advanced methodologies and statistical modeling to produce insightful reports essential for strategic planning

Utilize analytical tools and hypothesis-driven analysis to enhance machine learning projects

Implement cutting-edge AI techniques to boost data analysis efficiency

Perform data sampling

Minimum Qualifications/Requirements

Bachelor''s degree in Mathematics, Statistics, Computer Science, or related field; master''s degree preferred

Typically 5+ years of relevant quantitative and qualitative research, analytics, data science, or machine learning experience

Demonstrated experience with pricing optimization models and elasticity analysis

Experience and solid understanding of machine learning algorithms and relational databases

Experience with data science modeling, statistics, analytics, business intelligence, or data-driven business strategy

Proven experience in model measurement, monitoring, and tuning within a production environment

Experience managing and delivering complex and technical products

Experience performing statistical analysis and applying visualization techniques

Native-level proficiency/fluency in English

Preferred Skills

Knowledge of data models and structures, as well as database design tools and query languages

Knowledge of multiple programming languages and statistical analysis tools such as Python, C++, JavaScript, R, SAS, Excel, SQL, MATLAB, and SPSS

Knowledge of statistical and data mining techniques such as generalized linear models (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural networks (CNN), and recurrent neural networks (RNN)

Strong working knowledge of AI/ML techniques with the ability to flex between data science and applied ML roles as business needs evolve

Experience designing and executing pilot measurement strategies and evaluating experiment results at scale across multiple markets

Basic knowledge of AI, its potential roles in solving business problems, and the future trajectory of generative AI models

Demonstrated communication, collaboration, and stakeholder management skills to build strong relationships with IT and the business

Demonstrated data analysis and data modeling skills to identify patterns, trends, and solutions to complex problems

Basic technical leadership skills encompassing employee coaching, objective setting, and fostering professional development

Ability to clearly communicate concepts and present complex data findings to both technical and business executives

Ability to conduct statistical analyses and build models with advanced scripting languages

Attention to detail and ability to maintain data accuracy and integrity