Saif Hussain

Delivering advanced data science, causal inference, marketing analytics, and AI solutions across Fortune 100 clients in retail, healthcare, financial services, and CPG. Lead full lifecycle data science projects, from problem scoping and modeling, to executive insight delivery, experimentation strategy, and production deployment.

Marketing Performance & Behavioral Analytics

• Built full Marketing Mix Models (MMM) using Bayesian hierarchical regression (PyMC3) across 18 media channels (digital, retail, in-store, and influencers). Quantified channel elasticities and optimized spend reallocation, increasing ROI accuracy by 18% across campaigns.
• Developed Shapley-based Multi-Touch Attribution (MTA) using NetworkX, NumPy, and Markov chain removal effects over 100M+ customer interactions; improved attribution precision by 22%.
• Designed incrementality and causal impact models using Difference-in-Differences, Synthetic Control, and Propensity Scoring to isolate true lift from promotions and omnichannel campaigns.
• Conducted deep behavioral analytics using Spark (2B+ event logs) to uncover drivers of consideration-stage drop-off, informing retargeting strategies that improved engagement by 14%.
• Created quasi-experimental frameworks for marketing tests where randomization was not possible (e.g., regional lift tests); executed power calculations, parallel trends validation, and post-period causal estimation.
• Partnered with marketing, finance, creative, and consumer insights teams to translate modeling outputs into clear recommendations for spend optimization, audience selection, and messaging strategy.
• Coordinated cross-functional analytics alignment across engineering, marketing science, and BI to ensure a unified measurement approach and consistent KPI interpretation.

Marketing Measurement, Causal Inference & Experimentation

• Led end-to-end development of Next-Best-Action (NBA) and audience targeting models using contextual-bandit reinforcement learning; generated 7M incremental prescriptions, and 27% conversion lift.
• Designed MMM using TensorFlow Probability to quantify ROI across paid media, organic channels, CRM touches, and clinical outreach. Improved marketing forecast accuracy by 17%.
• Created Multi-Touch Attribution (MTA) pipelines integrating CRM, digital, and claims data; built Shapley-based attribution models with Markov chain effect analysis; improved channel contribution clarity by 19%.
• Built and executed experimentation frameworks: A/B tests with CUPED variance reduction, geo-lift experiments, sequential testing decision frameworks, and randomized controlled trials (RCTs) for outreach timing.
• Designed quasi-experiments (DiD, Synthetic Control) to measure incremental lift where RCTs were not feasible.
• Led causal confounder mapping using directed acyclic graphs (DAGs), and implemented mitigations through stratification and matched controls.
• Analyzed global behavioral patterns across 50M+ patient interactions using Spark, Presto, and Databricks to drive content strategy, channel sequencing, and message personalization.
• Partnered closely with Marketing BizOps, Consumer Insights, and Finance to present clear, executive-ready insights, informing a $40M+ annual budget allocation.
• Acted as the analytics lead for cross-functional pods, spanning marketing, data engineering, IT, and external vendor partners (IQVIA/Axtria).

Campaign Measurement & Behavioral Modeling

• Built propensity and uplift models using PySpark MLlib and XGBoost to optimize outreach campaigns; improved targeted campaign efficiency by 25%.
• Designed Bayesian time-series models to forecast campaign impact and wallet share growth under multiple spend scenarios.
• Implemented causal inference techniques (DiD, inverse probability weighting) to measure lift for email and inbound campaigns.
• Built and maintained large-scale distributed data pipelines (Spark, Hive, S3), powering multi-million customer engagement models.
• Delivered executive dashboards (Power BI/Tableau), highlighting incremental lift, spend effectiveness, and attribution metrics.
• Led cross-functional analytics alignment across marketing, risk, and finance.

• Built ETL pipelines and campaign analytics dashboards for CRM and digital channels; automated reporting reduced turnaround by 80%.
• Conducted segmentation and funnel analysis to improve campaign targeting precision.
• Developed data visualizations to present findings to stakeholders effectively.