Advanced Analytics Modeler
5 days ago
At Quantzig, we are a global analytics and advisory firm that has offices in the US, UK, Canada, China, and India.
We have assisted our clients across the globe with end-to-end advanced analytics, visual storyboarding, Machine Learning, and data engineering solutions implementation for prudent decision-making.
If you have what it takes to be a champion with business and functional skills to take ownership of an entire project end-to-end, help build a team with great work ethic and a drive to learn, you are the one we're looking for.
The clients love us for our solutioning capability, our enthusiasm, and we expect you to be a part of our growth story.
Find out more about the company:
Key Responsibilities- Lead the design and development of advanced Market Mix Models (MMM) using Bayesian frameworks.
- BUILD EFFICIENT AND SCALABLE PROBABILISTIC MODELING TOOLS TO SUPPORT MMM.
- Integrate econometric principles and advanced statistical techniques (e.g., hierarchical modeling, time-series analysis, causal inference) to improve MMM accuracy.
- Explore emerging methodologies like multi-touch attribution (MTA) and incrementality testing to complement MMM.
- Design and deploy automated MMM pipelines using Python and cloud platforms (e.g., AWS, Azure, or GCP).
- Programming & Libraries: Expert-level Python skills (e.g., PyMC3/PyMC4, NumPy, pandas, SciPy, Matplotlib, Plotly, Seaborn).
- Bayesian Modelling: Deep expertise in probabilistic programming and Bayesian frameworks; experience with libraries like Stan, TensorFlow Probability, is a plus.
- MMM Tools: Proficient in tools such as Google Meridian/LightWeightMMM, Uber Orbit, Bambi, and custom Bayesian implementations.
- Econometrics & Statistics: Strong grasp of econometric modeling, time-series analysis, causal inference, and hierarchical/multi-level modeling.
- Marketing Analytics: Advanced knowledge of media attribution, channel optimization, and marketing KPIs.
- Cloud Platforms: Hands-on experience with AWS (S3, Lambda, SageMaker), Azure, or GCP for scalable deployments.
Note: The salary range is based on industry standards and may vary depending on location and other factors.
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