Marketing Data Scientist - Growth Analytics & ML

DigitalXNode; Chicago (IL); On-site; USD 110,000 - 160,000; Full time

Job summary

DigitalXNode is seeking a Marketing Data Scientist to design and deploy advanced analytics for marketing ROI measurement and attribution. You will build data-driven solutions using Python, time series analysis, and ML in AWS, collaborating with marketing, data science, and engineering teams to translate insights into business impact.

Responsibilities include developing production-grade analytics apps with Streamlit, delivering scalable data pipelines, and presenting insights to stakeholders to

Qualifications

• Strong Python coding skills for data science and analytics.
• Experience with large marketing performance data sets.
• Solid background in statistics and predictive modeling.

Responsibilities

• Develop and maintain ROI measurement models for marketing initiatives.
• Analyze marketing, advertising, and sales data to identify growth opportunities.
• Measure channel performance across digital and traditional marketing.
• Evaluate advertising effectiveness and campaign performance with analytics.
• Translate insights into actionable recommendations for optimization.

Skills

Python programming for data science

Time series analysis

Marketing analytics

Statistical modeling

Bayesian modeling

Linear regression

Time series forecasting

Data interpretation & communication

Education

Bachelor's degree in a quantitative field

Master's degree preferred

Certifications in Data Science / AWS (advantageous)

Tools

Pandas

NumPy

PyTest

Streamlit

Scikit-learn

Statsmodels

Jupyter Notebooks

AWS

S3

EC2

Job description

DigitalXNode is seeking a Marketing Data Scientist to design and deploy advanced analytics for marketing ROI measurement and attribution. You will build data-driven solutions using Python, time series analysis, and ML in AWS, collaborating with marketing, data science, and engineering teams to translate insights into business impact.

Responsibilities include developing production-grade analytics apps with Streamlit, delivering scalable data pipelines, and presenting insights to stakeholders to