Require Data Science Architect //Location: Cincinnati, OH // Duration Long term Contract to Contract

[Amarinder singh]

Job Title: Data Science Architect – Warehouse Intelligence Platform

Location: Cincinnati, OH

Duration Long term

Contract to Contract 

 

 

Summary

As a Senior Data Scientist, you will be lead and architect for the intelligence layer for client’s Warehouse Intelligence Platform. Your mission would be to transform raw operational data into real-time, actionable decisions that optimize warehouse flow, labor efficiency, and equipment throughput. This role focuses on analyzing the operation data and creating AI+ML algorithms that will be productized into client’s Warehouse Intelligence Platform to improve labor efficiency, flow optimization, orchestration decisions, equipment performance, and near-real-time decision support.

This is a hands-on, self-starter leadership role: you’ll identify opportunities, define success metrics, shape roadmaps, and guide delivery from prototype to deployment.

 

Essential Job Duties

• Solution Architecture: Design and deploy end-to-end ML/AI architectures that integrate directly with client’s warehouse execution platform to solve complex logistics problems (e.g., wave management, task interleaving, and labor balancing).
• Customer-Facing PoC Delivery: Own the end-to-end build and demonstration of customer-facing AI proof-of-concepts, from data exploration through model validation to customer stakeholder presentation. PoCs include: Order Intelligence, Capacity Optimization Framework (portfolio-wide), Throughput Anomaly Detection, Demand Forecasting, and Labor Allocation Recommendations. Models must be scoped to demonstrate measurable customer value within a single PI. This role requires direct customer-facing engagement — candidate must be comfortable presenting AI findings and recommendations to customer operations leaders and executives.
• Optimization Modeling: Build and refine sophisticated models for order batching, wave management, pathfinding, slotting optimization, and task interleaving to minimize travel time, reduce exceptions, and maximize throughput across automated and manual warehouse environments.
• Predictive Intelligence: Develop forecasting models for warehouse volume, labor requirements, and equipment maintenance to prevent bottlenecks before they occur.
• Algorithm Integration: Work closely with Product Development and Partners teams to ensure ML models are performant enough for low-latency, real-time execution environments.
• MLOps Leadership: Establish best practices for model deployment, monitoring, and "re-training in the wild" to ensure systems adapt to changing seasonal demands.
• Generative Operational Intelligence: Build and deploy Large Language Models (LLMs) and Agentic workflows that allow operational leaders to query warehouse health and "ask" for optimization strategies in plain English.
• Define and maintain Warehouse Intelligence Platform analytics client’s: end-to-end cycle time, task latency, WIP, throughput, SLA adherence, pick rate, exceptions, and rework.
• Partner with Data Engineering to implement data pipelines from warehouse execution, WMS, WCS, and PLC/IoT systems to ensure that the data is AI ready.
• Build dashboards and operational insights for customers.
• Document models, assumptions, monitoring, and performance drift; implement governance and responsible AI practices.

 

Required Education, Skills, and Experience

• Master’s in Data Science, Computer Science, Industrial Engineering, Operations Research, etc.
• 7+ years in applied data science with production AI and ML
• Strong Python (pandas, Pytorch/ TensorFlow, scikit-learn), SQL, experience with experimentation and statistical inference.
• Ability to work with event-driven data (timestamps, state transitions, logs).
• Self-starter with the ability to investigate and understand business requirements, translate them into technical specifications, and implement the required design.
• Excellent problem-solving and analytical skills. Strong communication and collaboration skills.
• Demonstrated experience with production classification, forecasting, and anomaly detection algorithms (e.g., XGBoost, Random Forest, ARIMA/Prophet, LSTM, Isolation Forest) - not just familiarity with LLM-based tools.
• Familiarity with operational data sources including PLC/SCADA systems, historian databases, WES/WMS/WCS event logs, and sensor streams as inputs to ML pipelines.
• Use-case-first mindset: demonstrated ability to define a specific prediction target and identify required data before building infrastructure. Candidates who default to “build the platform first” are not the right fit for this role.

 

Other Requirements/Comments

• Familiarity with LLM orchestration, prompt engineering, and RAG (Retrieval-Augmented Generation) for operational intelligence use cases is a plus; primary focus of this role is operational ML, not generative AI.

• Experience with warehouse/fulfillment systems: WES/WMS/TMS, automation, labor management.
• Azure/Databricks experience: Databricks ML, Delta Lake, MLflow, feature engineering at scale.
• Experience deploying models into product workflows (API scoring, batch scoring, streaming signals).
• Strong background in Operations Research (OR), Linear Programming, or Reinforcement Learning

Thanks and Regards,
Amarinder Singh (Sr. IT Associate),
Kalven Technologies Inc. 1701, E.Wood Field Rd, Suite 300, Schaumburg, IL-60173,
Work : 312-667-0211 | Email id : amar...@kalventech.com
http://www.kalventech.com,
Product Engineering | Systems Integration | Professional Services.