We are seeking a Senior Data Scientist to support Acxiom's Measurement & Optimization offering, building and maintaining the models and analyses behind incrementality/lift, multi-touch attribution, marketing mix modeling, and rules-based attribution. The primary focus of this role is measurement methodology support.
You will work with large datasets that combine media, commerce, engagement, and outcome signals, helping turn ambiguous business questions into repeatable, standardized measurement products. Over time you will also work with identity-linked data, Real ID and clean rooms; identity and clean-room exposure is a valued secondary skill set. This is a strong opportunity for a data scientist who wants to build applied statistics/ML skills in a large-scale, privacy-safe analytics environment.
DUTIES AND RESPONSIBILITIES
Primary: Measurement methodology
- Support the design, build, and validation of incrementality/lift, MTA, MMM, and rules-based attribution models under the guidance of senior data scientists
- Perform exploratory data analysis and feature engineering on large datasets spanning media exposure, commerce, and engagement sources
- Collaborate with Data Engineering to validate data quality and feature pipelines feeding into models
- Support integration and testing of new partner/exposure data sources into modeling workflows
- Document methodologies and contribute to internal playbooks for repeatable measurement delivery
- Monitor deployed models and flag data or performance issues
Secondary: Identity and clean-room measurement
- Assist in applying privacy-preserving analytical techniques to deliver aggregate insights without exposing individual-level data
- Build and maintain scoring, segmentation, and propensity models as part of the broader identity graph analytics stack
QUALIFICATIONS
Required
- Masters degree in Statistics, Computer Science, Applied Mathematics, Economics, or a related field, plus 5+ years of experience in applied data science or analytics.
- Working knowledge of statistical modeling, hypothesis testing, and causal inference concepts
- Proficiency in Python and/or R and SQL
- Exposure to marketing measurement concepts (incrementality, attribution, MMM) and media metrics (impressions, reach/frequency, attribution windows), or demonstrated ability to learn quickly
- Comfort working with large, complex, multi-source datasets on cloud/data platforms (e.g., Snowflake, Databricks, AWS)
- Strong analytical and communication skills
Preferred
- Internship or prior work experience in AdTech/MarTech or a data-driven product environment
- Exposure to identity resolution, graph-based data structures, or clean room environments
- Familiarity with visualization tools (e.g., Tableau, Looker, Power BI)
- Exposure to partner/API data integrations with walled gardens or retail media platforms
- Coursework or project experience in causal inference or experimental design
- Exposure to LLMs, RAG, MCP, and AI coding assistants (e.g., Claude Code, GitHub Copilot) to accelerate analysis and documentation, with sound practices for validating AI-generated code and agentic or AI-assisted workflows
Primary Location City/State:
Homebased - Conway, Arkansas
Additional Locations (if applicable):
