Full-Time Data Scientist - Media Mix Optimization (MMO)

Project Overview: Media Mix Optimization (MMO)

Our MMO platform empowers clients with data-driven decision-making in marketing strategy. By applying Bayesian and frequentist approaches, we quantify channel-level ROI, measure incrementality, and simulate outcomes.

• Data Integration: Combining client data across digital, offline, and emerging channels.
• Model Development: Building and validating media mix models (MMM) using statistical and machine learning techniques.
• Scenario Simulation: Enabling stakeholders to forecast outcomes under different budget allocations.
• Deployment & Visualization: Using Streamlit to build interactive, client-facing dashboards.
• Scalability: Engineering the system to support multiple clients.

Responsibilities

1. Develop, validate, and maintain media mix models.
2. Engineer and optimize data pipelines.
3. Design, build, and deploy Streamlit-based dashboards.
4. Conduct exploratory data analysis (EDA) and advanced feature engineering.
5. Apply Bayesian methods, regularization, and time-series analysis.
6. Implement optimization and scenario-planning algorithms.
7. Collaborate with product, engineering, and client teams.
8. Present insights and recommendations to stakeholders.
9. Stay current with emerging tools and techniques.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field.
• Proven experience in media mix modeling or marketing analytics.
• Proficiency in Python and data science libraries (pandas, NumPy, scikit-learn, statsmodels, PyMC).
• Experience with Streamlit or equivalent frameworks.
• Proficiency in SQL.
• Solid foundation in statistical modeling and machine learning.
• Strong problem-solving and communication skills.

Preferred Qualifications

• Experience with Bayesian hierarchical models and time-series decomposition.
• Familiarity with cloud-based platforms (AWS, GCP, Azure).
• Experience with data visualization tools beyond Streamlit (Tableau, Power BI, D3.js, Plotly).
• Exposure to big data ecosystems (Spark, Hadoop).
• Knowledge of causal inference techniques.

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