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
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