Data Decision ScientistLocation: Remote United States (Florida preferred)
Contract: 12 months
Anticipated Start: November 1 or later
Pay Rate: $75/hr - $83/hour
Position Overview
We are seeking a
Data Decision Scientist to join a Decision Science team focused on using advanced analytics and mathematical modeling to inform business decisions.
This role sits at the intersection of
business strategy, advanced analytics, and technology and will focus on developing, deploying, and maintaining statistical and forecasting models that support media and advertising decision-making.
The ideal candidate combines a strong foundation in
statistics, forecasting, econometrics, and mathematics with the technical ability to operationalize models in production environments. This position requires someone who can take analytical solutions beyond model developmentfrom data preparation and validation through deployment, automation, and ongoing production support.
Key Responsibilities- Develop and maintain advanced statistical, forecasting, econometric, and predictive models.
- Build models for forecasting, simulation, estimation, and optimization.
- Perform data preparation, exploration, validation, model calibration, and testing.
- Analyze advertising impressions, audience trends, demographic performance, and related forecasting data.
- Build, deploy, and support production-grade data science solutions.
- Develop and maintain data pipelines supporting model deployment and automation.
- Apply software engineering best practices to model development, testing, version control, and deployment.
- Work within cloud-based environments and support CI/CD processes.
- Partner with business stakeholders to translate business and forecasting needs into scalable analytical solutions.
- Present analytical findings and recommendations to business partners.
- Support the ongoing integration and performance of models within business processes and production systems.
Required Qualifications- Master's or PhD in Statistics, Operations Research, Industrial Engineering, Mathematics, Machine Learning, Econometrics, or a closely related quantitative discipline.
- Strong theoretical foundation in mathematics, statistics, and quantitative modeling.
- Advanced expertise in statistics, forecasting, and econometrics.
- Strong programming skills in Python and SQL.
- Experience working with relational databases; Snowflake experience is preferred.
- Hands-on experience building and deploying models in production environments.
- Experience developing and supporting production data pipelines.
- Experience with cloud-based environments and CI/CD processes.
- Working knowledge of Docker and GitHub and/or GitLab.
- Ability to operationalize data science models within enterprise software environments.
- Relevant statistical and modeling experience may include time-series modeling, Bayesian statistics, generalized linear models, panel data, instrumental variables, machine learning, neural networks, random forests, boosting, clustering, or related advanced quantitative methods.
Preferred Qualifications- PhD in a highly quantitative discipline.
- Media or advertising industry experience.
- Experience with audience measurement, linear television, or advertising analytics.
- TV viewership or audience forecasting experience.
- Experience with advertising impressions, audience demographics, or media consumption datasets.
- Revenue management, demand forecasting, or pricing analytics experience.
- Experience translating advanced analytical research into scalable, sustainable business solutions.
Ideal Candidate
The ideal candidate is a highly quantitative data scientist who also brings strong production and software engineering capabilities. You should be comfortable independently designing sophisticated statistical models while also taking responsibility for deploying, automating, and supporting those models in production.
Success in this role requires both deep analytical expertise and practical implementation skills, along with the ability to communicate complex analytical concepts and results to business stakeholders.