The Trade Desk is seeking a Data Scientist II to analyze complex consumer behavior data related to internet users, uncover meaningful relationships, and develop models that enhance their advertising solutions. The insights will directly influence the development of predictive algorithms and data-driven strategies that improve targeting, bidding, and campaign optimization.
Requirements
- Proficiency in programming languages such as Python or R, and experience with data manipulation and analysis tools like SQL, Spark, or Hadoop.
- Familiarity with cloud platforms (AWS, GCP, Azure) and data visualization tools (Tableau, Power BI) is advantageous.
- Proven experience (typically 3+ years) in data science, analytics, or a similar domain, with a strong portfolio of projects involving consumer data analysis, predictive modeling, and machine learning techniques.
- Strong analytical and problem-solving skills
- Knowledge of digital advertising, consumer behavior analysis, and familiarity with ad tech platforms will be considered a plus.
Responsibilities
- developing and implementing advanced analytical models to interpret consumer behavior data, identifying key relationships and patterns that influence advertising effectiveness.
- designing experiments, analyzing large datasets, and creating scalable machine learning models to optimize ad targeting and bidding strategies.
- maintaining and improving existing models, ensuring their accuracy and relevance over time.
- communicating insights and findings to technical and non-technical stakeholders through reports and presentations.
- Staying current with industry trends and emerging technologies in data science and digital marketing is vital to continuously enhance our analytical capabilities.
Other
- a bachelor's degree in Computer Science, Data Science, Statistics, or a related field; a master's degree or higher is preferred.
- excellent communication abilities
- the capacity to work collaboratively in a fast-paced environment
- telecommuting options are available, allowing for flexible work arrangements within the normal commuting distance of our office.
