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# Data Scientist

- **Company:** Digitas, Inc.
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $88,540.0
- **Contract:** Temporary contract
- **Skills:** Midjourney, Amazon Web Services, Data Mining, Linux, Distributed Systems, R (Programming Language), Apache Hadoop, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, SQL Databases, Apache Spark, Generative AI, Information Technology, GPT, Unsupervised Learning
- **Published:** August 11, 2026
- **Apply:** https://careers-publicisgroupe.icims.com/jobs/169066/job

## About the Role

We're looking for rigorous analytic training and 3+ years professional experience in a data science or analytics role, which typically includes:

 * 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.
 * 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.

## Description

* 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.
 * Applying 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.


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