Role Overview
This role is for a hands-on Data Science contributor who is actively involved in end-to-end modelling, coding, analysis, and insight generation. The focus is on marketing analytics, econometrics, and attribution modelling, with strong fundamentals in statistical modelling and MMM. The role also supports the use of AI/ML and genetic/agentic AI to enhance productivity, automation, and insight accuracy.
Key Responsibilities
Develop and execute marketing analytics models including MMM, attribution, and regression models.
Perform statistical modelling, and advanced analysis for marketing effectiveness.
Lead hypothesis-driven experimentation and lift analysis (A/B tests, brand studies, holdout samples).
Build dashboards and automated reporting utilities to operationalize insights.
Leverage AI/ML and selective genetic/agentic AI methods for automation, summarization, and insight generation.
Collaborate with cross-functional teams including analytics, engineering, and media strategy.
Embed genetic/agentic AI (LLMs, AutoML) into analytics workflows—rapid prototyping, feature generation, insight summarization.
Conduct cardinal insights presentations and reporting, helping business teams interpret model outputs and translate results into optimize campaign strategy.
Required Skills
3–6 years in applied data science, media analytics, or marketing analytics in agency or consulting environments.
Expertise in Python (pandas, scikit-learn, PyTorch/TensorFlow), SQL, and ML frameworks.
Experience building dashboards (Power BI, Looker Studio, Data Studio).
Hands-on experience and knowledge on media measurement techniques like Media Mix Modelling, Multi-Touch Attribution, Competitive
Proficiency with generative AI tools (HuggingFace, OpenAI, LangChain) to accelerate data processing and modelling.
Maintain model accuracy through continuous monitoring, retraining, and performance tracking.
Good to Have
Perform competition analysis by ingesting and modelling external media, audience, and pricing data.
Knowledge of cloud platforms (GCP, AWS, Azure), containerization (Docker/Kubernetes), and CI/CD pipelines.
Strong practical experience building ML models, including lift testing, MLM/regression, time-series forecasting, and customer segmentation.
Collaborate with product and engineering teams to productionize ML models and tool ensuring cloud-based deployment with CI/CD, monitoring, containerization.
Familiarity with marketing/advertising data domains including campaign, impression, attribution, and media spend data.
Define data taxonomies and metadata standards to support model governance and reproducibility.
Experience with advanced causal frameworks (Shapley, uplift models, robo-hoc methods).
Experience with model orchestration tools (Airflow, Prefect) and monitoring frameworks (MLflow, Seldon).
Knowledge of data governance, taxonomy design, and model interpretability frameworks.
Personal Attributes
Strong analytical curiosity, precision, and attention to detail.
Ability to translate complex results into clear business insights.
Collaborative and comfortable working across cross-functional teams including analytics, engineering, product, and strategy.
Proactive, adaptive, and thrives in fast-moving agency environments.
