Senior Data Scientist

Digitas•New York, NY

40d

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 Senior Data Scientist, you'll solve 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 mining data for decision-focused insights.
Hands-on experience running common statistical or machine learning procedures, such as descriptive statistics, hypothesis testing, dimension reduction, feature transformation, supervised or unsupervised learning.
Hands-on experience using Python or R, SQL, and distributed computing systems such as Hadoop or AWS. Familiarity with Linux and/or Spark preferred.
Demonstrated interest in marketing analytical 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 and reframing marketing and business questions into analytical plans.
Using distributed computing systems to ingest, access and integrate disparate big data sources.
Conducting extensive exploratory analysis to identify relevant insights, useful transformations and analytical applications.
ing quantitative techniques, including statistical and machine learning, to uncover latent patterns in the data.
Building and testing scalable data pipelines or models for real-time applications.
Summarizing, visualizing, communicating and documenting analytic concepts, processes and results for technical and non-technical audiences.
Collaborating with internal and external stakeholders to establish clear analytical objectives, approaches and timelines.
Sharing knowledge, debating techniques, and conducting research to advance the collective knowledge and skills of our Data Science practice.