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# Data Scientist

- **Company:** Digitas, Inc.
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Salary:** $88,540.0
- **Contract:** Temporary contract
- **Skills:** Data Mining, Linux, Apache Hadoop, Python (Programming Language), Machine Learning, Regression Analysis, SQL Databases, Cloud Platform System, Apache Spark, Generative AI, Information Technology, Google Gemini, GPT
- **Published:** October 11, 2026
- **Apply:** https://careers-publicisgroupe.icims.com/jobs/176583/job

## About the Role

* Hands-on experience mining data for decision-focused insights.
 * Expertise in statistical and analytical methods, especially regression analysis, causal inference, experimentation and machine learning.
 * Hands-on experience with Python or R and SQL, and with distributed or cloud computing platforms such as Hadoop or GCP. Familiarity with Spark and/or Linux is preferred.
 * Demonstrated interest in applying analytics to marketing problems such as media optimization, customer experience, segmentation and targeting.
 * A self-starter who thrives in a fast-paced, flat organization.
 * Comfort with ambiguity and the judgment to bring structure and process where none yet exists.
 * Familiarity with generative AI tools such as ChatGPT or Google Gemini, and with prompt-based workflows, is a plus, especially for coding, research, ideation and content generation.
 * 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 is preferred

## Description

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., * 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 methods, 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.