Website:
palirainc.com
Job details:
Manager, Analytics & Data Science
The Opportunity
Lead end-to-end analytical engagements in CPG and Healthcare—from problem definition to production deployment. Own forecasting, market mix modeling, pricing optimization, and omnichannel analytics. Deliver impact with real data constraints: messy POS, tight timelines, skeptical stakeholders. Build a portfolio of high-impact projects and master the trade-offs between perfect models and shipped solutions.
What You'll Do Daily
● Define analytical problems—translate vague requirements into measurable modeling objectives. Push back on unrealistic requests.
● Design solutions—choose approach (forecasting, MMM, optimization), set success metrics, identify data dependencies (Nielsen/Circana feeds, POS, transaction logs).
● Build & validate models—prototype in Python, backtest on CPG/Healthcare data, quantify uncertainty, document assumptions.
● Deploy to production—integrate with engineering teams, deploy to Fabric/AWS Redshift/Databricks, maintain involvement through go-live.
● Iterate & own outcomes—train stakeholders, respond to feedback, improve based on real-world performance.
● Bridge business & engineering—explain technical constraints to executives, translate requirements to engineers.
Problem domains: demand sensing, statistical forecasting, market mix modeling, multi-channel attribution, pricing optimization, omnichannel dynamics, causal inference.
What We Look For
Analytical Maturity
You ship models stakeholders use. You know backtesting pitfalls, understand why models fail in production, and defend your choices to skeptical stakeholders.
Business Judgment
You ask why before you build. In CPG/Healthcare contexts, you understand POS dynamics, trade promotion mechanics, omnichannel conflicts, media elasticity. You dig into business mechanics, not surface requirements. You own outcomes—whether your forecast prevents stockouts, your MMM optimizes spend, or your model prevents channel cannibalization.
Technical Depth
You've shipped forecasts, market mix models, causal inference models on real CPG/Healthcare data. You code in Python (scikit-learn, statsmodels) or R (forecast, tidyverse). You write SQL, understand ETL pipelines, deploy to cloud (AWS Redshift, Databricks, Fabric). You know latency vs. accuracy trade-offs. You've built Bayesian forecasts, MMM with adstock effects, real-time demand signals. You stay current—read papers, test ideas, know what actually works in CPG/Healthcare analytics.
Background We Value
● 3-5+ years statistical modeling on POS/transaction/consumer data in CPG, Healthcare, or retail.
● Experience with Nielsen, Circana, SPINS, IMS Health, Payer databases.
● Proficient in Python, R, or Bayesian modeling for forecasting, attribution, optimization.
● Strong SQL and data engineering—write queries, understand ETL, know cloud basics (AWS, Redshift, Databricks, Fabric).
● Shipped production models. Understand deployment constraints.
● Bonus: Market mix modeling, real-time deployment, omnichannel analytics, Excel modeling.
Why Join
Growth: Build a portfolio of high-impact CPG and Healthcare projects. Real data challenges, real impact.
Autonomy: Own engagements end-to-end. No approval chains.
Domain Expertise: Learn from analytical leaders who've shipped CPG and Healthcare projects.
To apply, please fill out this form:
[https://forms.gle/mT76ofT3PimRrGrEA]
Click on Apply to know more.