Supply Chain Data Analyst / Data Scientist

We are seeking a highly motivated and hands-on Data Scientist / Senior Data Engineer to help build and scale KPI's Closed-Loop Design Program - an initiative focused on accelerating pre-sales engineering, improving automation design accuracy, and leveraging AI-driven insights across the customer solution lifecycle.

- Data Engineering - Applied AI & Analytics - Supply Chain Automation - Solution Design

You will work closely with solution architects, engineering teams, and business stakeholders to transform RFPs, customer requirements, and historical project data into intelligent, repeatable outputs that drive faster and more accurate proposal development.

This is an exciting opportunity for someone who enjoys building systems from the ground up, solving complex operational problems, and influencing how advanced automation solutions are designed and delivered.

- RFP & Specification Intelligence Extract requirements, generate compliance matrices, and identify clarifying questions automatically. - Cost & ROI Scenario Modeling Support data-driven decision-making through option comparisons and sensitivity analysis. - Proposal "Fact Pack" Automation Create scalable executive summaries and SOW-ready proposal content from governed datasets. - Design & Operational Analytics Analyze throughput, staffing models, slotting strategies, and replenishment impacts. - Benchmark-Driven Solution Guidance Leverage historical project data and comparable implementations to guide future designs.

- Build and maintain curated datasets using Databricks (Delta tables, jobs, SQL Warehouses) and SQL-based governed reporting tables. - Ingest and manage full pursuit lifecycle artifacts including RFPs, specifications, drawings, and addenda with proper metadata and version control. - Develop applied analytics solutions including:

- Forecasting - Scenario modeling - Optimization (LP/MILP or practical heuristics) - Similarity modeling and benchmarking

- Publish reliable datasets and insights for Tableau Cloud consumption with consistent business definitions. - Establish a closed-loop learning cycle by comparing design assumptions against operational outcomes to continuously improve benchmarks and estimate accuracy. - Partner cross-functionally with engineering, analytics, and solution design teams to improve automation decision-making at scale.

- Accelerate proposal development with intelligent, governed "first draft" solution packs - Improve confidence and accuracy in complex automation pursuits - Build scalable benchmarking capabilities that improve operational estimates over time - Create data products that directly influence automation strategy and customer outcomes

- Data Science - Analytics Engineering - Applied Machine Learning with proven production delivery experience.

- Data modeling - Query optimization - Governance-ready dataset development

- Hands-on experience with Databricks (Delta Lake, notebooks, jobs, SQL Warehouses) or similar enterprise data platforms. - Strong Python experience preferred for:

- Ability to communicate assumptions, risks, and analytical outcomes clearly to technical and non-technical stakeholders.

- Experience in warehouse automation or intralogistics environments:

- AMRs - AutoStore - Shuttle systems - Sortation - WES/WCS platforms

- Forecasting - Optimization - Simulation / Digital Twin technologies - Decision-support analytics

- Familiarity with document parsing and extraction workflows for RFP/specification processing. - Tableau experience including:

- Curated dataset publishing - Dashboard performance optimization

At KPI Solutions, you'll be part of a team that values innovation, accountability, and continuous improvement. This is more than a reporting role-it's an opportunity to make a measurable impact on project success and business performance.