Data Scientist

Project Overview: Media Mix Optimization (MMO)

Our MMO platform is an in-house initiative designed to empower clients with data-driven decision-making in marketing strategy. By applying Bayesian and frequentist approaches to media mix modeling, we can quantify channel-level ROI, measure incrementality, and simulate outcomes under various spend scenarios.

Key components of the project include:

• Data Integration: Consolidating client first-party, third-party, and campaign-level data across digital, offline, and emerging channels into a unified modeling framework.
• Model Development: Constructing and validating media mix models (MMM) using advanced statistical and machine learning techniques, including hierarchical Bayesian regression, regularized regression (Ridge/Lasso), and time-series modeling.
• Scenario Simulation: Enabling stakeholders to forecast outcomes under different budget allocations through simulation and optimization algorithms.
• Deployment & Visualization: Utilizing Streamlit to build interactive, client-facing dashboards for model exploration, scenario planning, and delivering actionable recommendations.

Scalability: Engineering the system to support multiple clients across industries with varying data volumes, refresh cycles, and modeling complexities.

Responsibilities

• Develop, validate, and maintain media mix models to evaluate cross-channel marketing effectiveness and return on investment.
• Engineer and optimize end-to-end data pipelines for ingesting, cleaning, and structuring large, heterogeneous datasets from multiple marketing and business sources.
• Design, build, and deploy Streamlit-based interactive dashboards and applications for scenario testing, optimization, and reporting.
• Conduct exploratory data analysis (EDA) and advanced feature engineering to identify performance drivers.
• Apply Bayesian methods, regularization, and time-series analysis to enhance model accuracy, stability, and interpretability.
• Implement optimization and scenario-planning algorithms to recommend budget allocation strategies that maximize business outcomes.
• Collaborate closely with product, engineering, and client teams to align technical solutions with business objectives.
• Present insights and recommendations to senior stakeholders in both technical and non-technical language.
• Stay current with emerging tools, techniques, and best practices in media mix modeling, causal inference, and marketing science.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, or a related field.
• Proven hands-on experience in media mix modeling, marketing analytics, or econometrics.
• Strong proficiency in Python and essential data science libraries (pandas, NumPy, scikit-learn, statsmodels, PyMC or similar Bayesian frameworks).
• Experience with Streamlit or equivalent frameworks (Dash, Shiny) for building data-driven applications.
• Proficiency in SQL for querying, joining, and optimizing large-scale datasets.
• Solid foundation in statistical modeling, regression techniques, and machine learning.
• Strong problem-solving skills with the ability to structure ambiguous business problems into data-driven solutions.
• Excellent verbal and written communication skills to translate technical outputs into business decisions.

Preferred Qualifications

• Experience with Bayesian hierarchical models, time-series decomposition, and marketing attribution approaches.
• Familiarity with cloud-based platforms (AWS, GCP, Azure) for data processing, model training, and deployment.
• Experience with data visualization tools beyond Streamlit (Tableau, Power BI, D3.js, Plotly).
• Exposure to big data ecosystems (Spark, Hadoop) for large-scale data processing.
• Knowledge of causal inference techniques (propensity scoring, uplift modeling, geo-experiments).