Lead Marketing Mix Modeling initiatives using statistical and machine learning techniques to evaluate channel performance, calculate ROI and MROI, optimize advertising budgets, and support marketing decisions. Build regression, Bayesian, and hierarchical models in Python; create stakeholder visualizations; and manage model deployment, monitoring, and maintenance through MLOps practices.
Data Scientist (MMM)
Summary
We are seeking an experienced Data Scientist to lead the development and implementation of advanced Marketing Mix Modeling (MMM) strategies within the IT Services sector. In this pivotal role, you will leverage sophisticated statistical techniques and machine learning to quantify channel contributions, optimize advertising spend, and drive data-informed marketing decisions. This position is critical for enhancing return on investment (ROI) and maximizing marketing efficiency through rigorous scenario simulation and budget optimization.
Responsibilities
- Design and execute complex Marketing Mix Modeling frameworks to evaluate the effectiveness of various marketing channels.
- Analyze Adstock effects and calculate channel-specific contribution metrics, including Return on Marketing Investment (MROI).
- Develop and run incremental budget optimization scenarios to simulate potential outcomes and guide strategic financial planning.
- Create high-quality, interactive visualizations using libraries such as Matplotlib, Seaborn, and Plotly to communicate insights to stakeholders.
- Build, train, and validate robust statistical models, including regression, Bayesian, and hierarchical approaches, using Python, Pandas, NumPy, and Scikit-learn.
- Oversee the end-to-end lifecycle of machine learning models, ensuring seamless deployment, continuous monitoring, and maintenance through MLOps best practices.
Requirements
Requirements:
- Minimum of 7 years of professional experience in data science, analytics, or a related quantitative field.
- Proven expertise in Marketing Mix Modeling (MMM) and a deep understanding of its application in modern marketing ecosystems.
- Strong proficiency in statistical modeling, specifically in regression analysis, Bayesian inference, and hierarchical modeling techniques.
- Advanced technical skills in Python, including extensive experience with data manipulation libraries (Pandas, NumPy) and machine learning frameworks (Scikit-learn).
- Demonstrated ability to implement Adstock transformations and calculate channel-level ROI and MROI.
- Competence in building and managing MLOps pipelines for model deployment and performance monitoring.
- Exceptional data visualization capabilities using Matplotlib, Seaborn, and Plotly to translate complex data into actionable business intelligence.
Compensation: ₹15-18 LPA
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