Tiger Analytics - AI/ML Engineer - Marketing Mix Modeling

We are seeking a skilled AI/ML Engineer (Marketing Mix Modeling - MMM) with 4+ years of experience to develop and deploy machine learning solutions that optimize marketing performance and business outcomes.

The ideal candidate should have expertise in Marketing Mix Modeling (MMM), Machine Learning, Statistical Modeling, Python, and Data Science, with experience in building scalable analytics solutions and delivering actionable insights for marketing effectiveness.

You will collaborate with data scientists, marketing teams, and business stakeholders to develop advanced models that measure marketing ROI, forecast business performance, and optimize media investments.

Key Responsibilities :

- Design, develop, and deploy Marketing Mix Models (MMM) to evaluate the effectiveness of marketing campaigns and media spend.

- Build machine learning and statistical models to optimize marketing performance and business growth.

- Collect, clean, and preprocess large datasets from multiple marketing and business sources.

- Perform feature engineering, exploratory data analysis (EDA), and model validation.

- Develop predictive models for sales forecasting, customer response, demand forecasting, and marketing attribution.

- Optimize media allocation and budget planning using advanced analytics techniques.

- Build scalable data pipelines and production-ready ML solutions.

- Deploy and monitor machine learning models in production environments.

- Collaborate with marketing, analytics, product, and engineering teams to translate business requirements into AI-driven solutions.

- Develop dashboards and visualizations to communicate model outputs and business insights.

- Stay updated with advancements in AI, machine learning, marketing analytics, and cloud technologies.

Required Skills :

- Strong proficiency in Python and SQL.

- Hands-on experience with Marketing Mix Modeling (MMM).

- Strong knowledge of Machine Learning, Statistical Modeling, and Predictive Analytics.

- Experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar frameworks.

- Strong understanding of regression modeling, time-series analysis, forecasting, and causal inference.

- Experience working with large datasets and data preprocessing techniques.

- Knowledge of marketing analytics, media measurement, attribution, and campaign performance analysis.

- Experience with data visualization tools such as Power BI, Tableau, or Matplotlib.

- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).

- Strong analytical, problem-solving, and communication skills.

(ref:hirist.tech)