Data Scientist

Digitas•New York, NY

31d

About The Position

Our Data Scientists deliver analytic solutions across a wide variety of client applications. We build inferential and predictive models, including machine learning algorithms and AI; we process, integrate and manipulate big data with distributed systems and customer data pipelines; we synthesize results and translate findings into compelling stories that resonate with clients. As a Data Scientist, you'll address complex marketing and business challenges—from cross-channel media and customer experience optimization to segmentation, targeting and business strategy—by accessing, integrating, manipulating, mining and modeling a wide array of data sources.

Requirements

A Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, econometrics, operations research, data science, computer science, engineering, marketing or social science methods.
Hands-on experience analyzing data for patterns and insights.
Hands-on experience running basic statistical or machine learning procedures, such as descriptive statistics, hypothesis testing, supervised or unsupervised learning.
Hands-on experience with Python or R. Familiarity with SQL, Linux, and/or distributed computing systems such as Hadoop or AWS preferred.
Demonstrated interest in marketing analytic applications.
Demonstrated self-starter who thrives in a fast-paced environment with flat structure.

Nice To Haves

Familiarity with prompt-based interaction and commonly used generative AI tools (e.g., ChatGPT, Google Gemini, DALL·E, Midjourney) is a plus, especially for tasks like ideation, research, or content generation.

Responsibilities

Translating marketing and business questions into data queries.
Using distributed computing systems to access and manipulate big data sources.
Conducting exploratory analysis to identify relevant insights and useful transformations.
ing quantitative techniques to uncover latent patterns in the data.
Interpreting data, analytical results and visuals to generate conclusions and recommendations.
Testing scalable data pipelines or models for real-time applications.
Ensuring that analytic tasks and results meet established objectives and timelines.
Sharing knowledge and conducting research to advance the collective knowledge and skills of our Data Science practice.