The Role
Develop marketing mix models, Bayesian statistical models, causal inference frameworks, attribution solutions, and experimentation methods to measure marketing effectiveness. Analyze large-scale marketing data, build Python analytics pipelines, create dashboards, and communicate insights for budget allocation and marketing optimization. Collaborate with data science, engineering, media strategy, and business teams while presenting findings, assumptions, uncertainty, and recommendations to stakeholders.
Summary Generated by Built In
Job Description
Key Responsibilities
- Develop, implement, and optimize Marketing Mix Models (MMM) to measure the impact of marketing investments across channels and support budget allocation decisions.
- Build robust Bayesian statistical models for marketing effectiveness, forecasting, uncertainty estimation, and scenario planning.
- Apply causal inference methodologies to measure the incremental impact of marketing campaigns and distinguish correlation from causation.
- Design and execute advanced statistical modelling techniques including regression analysis, hierarchical Bayesian models, time-series analysis, and probabilistic modelling.
- Develop attribution and incrementality measurement frameworks using experimental and observational data.
- Conduct hypothesis-driven experimentation, including A/B testing, geo experiments, holdout testing, and lift measurement.
- Analyze large-scale marketing and media datasets to generate actionable business insights.
- Build automated dashboards and reporting solutions using Power BI or Looker Studio.
- Collaborate with Data Science, Engineering, Media Strategy, and Business teams to translate analytical findings into marketing optimization strategies.
- Build scalable Python-based analytics pipelines for model development, validation, monitoring, and reporting.
- Present statistical findings and business recommendations to stakeholders with clear explanations of assumptions, confidence intervals, and model limitations.
Required Skills
Experience
- 3–6 years of experience in Marketing Analytics, Marketing Science, Applied Data Science, Econometrics, or Media Analytics.
- Strong experience working in agency, consulting, or digital marketing analytics environments.
Core Technical Skills
- Expert knowledge of Marketing Mix Modelling (MMM).
- Strong understanding of Bayesian Inference and Bayesian statistical techniques.
- Strong expertise in Statistical Modelling including:
- Linear Regression
- Multivariate Regression
- Hierarchical Models
- Time-Series Models
- Econometric Modelling
- Hands-on experience with Causal Inference methodologies such as:
- Difference-in-Differences
- Synthetic Control
- Propensity Score Matching
- Instrumental Variables
- Uplift Modelling
- Strong Python programming skills using:
- pandas
- NumPy
- SciPy
- scikit-learn
- PyMC / PyMC3
- Statsmodels
- Strong SQL skills.
- Experience with Power BI or Looker Studio.
Preferred Skills
- Experience with Google Meridian Marketing Mix Modeling Framework.
- Experience building Bayesian MMM models using Meridian.
- Knowledge of GeoLift, LightweightMMM, Robyn, or other modern MMM frameworks.
- Experience with GCP, BigQuery, Vertex AI, or cloud-based analytics platforms.
- Knowledge of MLflow, Airflow, Docker, and CI/CD.
- Familiarity with Generative AI for reporting automation and insight generation.
Must-Have Keywords for Screening
- Marketing Mix Modeling
- MMM
- Bayesian
- Bayesian Inference
- PyMC
- PyMC3
- Statistical Modeling
- Econometrics
- Causal Inference
- Incrementality
- Regression
- Statsmodels
- Meridian
- Google Meridian
- LightweightMMM
- Robyn
Skills Required
- 3-6 years of experience in marketing analytics, marketing science, applied data science, econometrics, or media analytics
- Experience in agency, consulting, or digital marketing analytics environments
- Expert knowledge of Marketing Mix Modeling
- Strong understanding of Bayesian inference and Bayesian statistical techniques
- Expertise in statistical modeling, regression, hierarchical models, time-series models, and econometric modeling
- Hands-on experience with causal inference methodologies, including difference-in-differences, synthetic control, propensity score matching, instrumental variables, and uplift modeling
- Strong Python programming skills using pandas, NumPy, SciPy, scikit-learn, PyMC or PyMC3, and Statsmodels
- Strong SQL skills
- Experience with Power BI or Looker Studio
- Experience with Google Meridian Marketing Mix Modeling Framework
- Experience building Bayesian MMM models using Meridian
- Knowledge of GeoLift, LightweightMMM, Robyn, or other modern MMM frameworks
- Experience with GCP, BigQuery, Vertex AI, or cloud-based analytics platforms
- Knowledge of MLflow, Airflow, Docker, and CI/CD
- Familiarity with generative AI for reporting automation and insight generation
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The Company
What We Do
Wildnet Technologies is an award-winning digital marketing and SEO agency providing AI-powered SEO, performance marketing, white-label marketing execution, and IT services. Its offerings also include web and mobile application development, e-commerce and ERP solutions, link building, content strategy, and paid advertising. The company helps brands and agencies expand their online reach, improve rankings, generate traffic, and build scalable digital growth engines.
