Note: The job is a remote job and is open to candidates in USA. NewAge Products Inc. is a manufacturer and distributor of premium home improvement solutions. The Sr. Manager, Order Fulfillment & Data Analytics owns the end-to-end order lifecycle and leads automation, AI/ML, fulfillment optimization, cost reduction, delivery acceleration, analytics, and cross-functional accountability. The role also builds and develops teams spanning order management operations and data analytics.
Responsibilities
- Lead the business case, vendor evaluation, selection, and implementation of an in-house or third party OMS and TMS platform; own the ROI model, BRD, technical specifications, and PMO delivery plan
- Migrate all order-import, validation, allocation, and routing logic from external or manual handling into the owned platform, with version control, audit trail, and documented decision rules
- Partner with IT and external development resources on architecture, API/EDI integration, ERP interoperability, and data model design; act as business owner and final arbiter of order logic
- Build a centralized datastore consolidating order, inventory, freight, carrier, retailer, and financial data as the single system of record; treat master data quality as a prerequisite, since automation performs only as well as the data behind it
- Baseline touchless performance and build a taxonomy of manual-touch reasons, quantified by volume, labour cost, and cycle-time impact
- Design, deploy, and continually tune the rule sets, tolerances, and exception logic that release orders without human intervention — and sustain that performance as volume, channel mix, and retailer requirements change
- Apply AI/ML to order screening: anomaly and fraud detection, address and data-quality auto-correction, predictive hold scoring, and auto-resolution of recurring exception types
- Deploy AI-assisted triage for orders that still require review, feeding every resolution back into the rules engine; treat each recurring manual touch as a defect with a named owner and closure date
- Define and operationalize the “ideal shipment” standard — complete, single shipment, correct node, correct cartonization, correct service level, no rework — as a measured, reportable rate
- Attack the drivers of non-ideal shipments: splits, short ships, wrong-node allocation, oversized cartons, avoidable expedites, re-ships, and returns caused by fulfillment error
- Optimize inventory positioning and allocation logic so orders fill complete from the node closest to the consumer; partner with Demand Planning and Inventory on placement, not just execution
- Deploy AI-driven cartonization and packing optimization to cut dimensional weight, transit damage, and material spend; automate pre-shipment validation against retailer routing guides, ASN, and labelling requirements
- Own cost-to-serve visibility at the order, SKU, channel, retailer, carrier, and lane level; make margin leakage visible and attributable
- Implement AI-assisted rate shopping and carrier selection that optimizes landed cost against the service commitment, rather than defaulting to incumbents or static routing tables
- Reduce the cost of failure — re-ships, expedites, chargebacks, damage claims, returns processing — by eliminating the upstream errors that generate them; build predictive modelling that flags orders and programs trending below target margin before they ship
- Prepare and manage department labour and SG&A budgets; negotiate and manage 3PL, carrier, and technology agreements and hold partners to contracted rates and service levels with data
- Own order cycle time end to end — receipt to release, release to pickup, pickup to doorstep — and drive down each segment independently
- Eliminate the internal delays that consume transit days: slow order release, hold queues, wave timing, inventory inaccuracy, and late carrier pickups
- Deploy predictive delay detection that flags at-risk orders in flight and triggers automated intervention and proactive customer communication before the promised date is missed
- Improve delivery-promise accuracy at the point of order; manage carrier and 3PL performance against transit commitments and reallocate volume based on measured lane-level results
- Lead automated dashboards and self-serve reporting that give each function line-of-sight to the metrics it controls, with drill-down to the transaction level
- Deliver recurring gap analysis quantifying where internal processes and external partners fail against standard, ranked by financial impact; convert findings into a prioritized action register with named owners and due dates
- Chair a recurring cross-functional operating review; hold Logistics, Customer Service, Sales, IT, Finance, and external partners accountable to committed improvements and escalate stalled items
- Use AI to accelerate the analysis itself — natural-language querying of the datastore, automated root-cause narratives, anomaly alerting, generated executive summaries — so the team spends its time on decisions, not report production
- Hire, develop, and lead a team spanning order management operations and data analytics; build succession depth in both disciplines
- Raise the automation and AI fluency of the department — tooling, standards, and safe-use guidelines so analysts and coordinators build and use automation daily
- Maintain workflows, process documentation, technical specifications, and SOPs; systematize department knowledge rather than leaving it resident in individuals
- Set individual objectives tied to department outcomes; coach to performance and hold the team to the same accountability standard applied cross-functionally. Other related tasks as assigned
Skills
- Post-secondary degree in Business, Supply Chain, Statistics, Computer Science, or a related field
- Minimum 10 years managing ecommerce and/or retail order management and fulfillment processes, in a highly analytical, metrics-driven environment; minimum 5 years leading and developing a team
- Demonstrated experience implementing or insourcing an OMS and/or TMS platform — vendor selection, integration, and post-go-live optimization
- Proven track record automating manual fulfillment processes with measurable improvement in cost per order, shipment quality, or delivery speed
- Strong working knowledge of ERP systems, API and EDI transactions, WMS/3PL integration, and order management architecture
- Advanced analytical capability — SQL and BI tooling (Power BI or equivalent) required; advanced Microsoft Office Suite skills mandatory
- Hands-on experience applying AI/ML to operational problems — forecasting, classification, anomaly detection, optimization, or generative AI
- Experience managing 3PL, carrier, and technology vendor relationships, including contract negotiation and SLA enforcement
- Travel may be required based on current or future business needs within Canada and the US
- Python/R and data modelling strongly preferred
- Experience deploying AI into a live operational workflow is a significant asset
Benefits
- Bonus based on experience
- Benefits Package
- An opportunity within a growing, fast-paced company, well established in its industry.
- Fun! We celebrate successes and hold special events to build strong teams.
- Our teams work under a remote-first Hybrid model, with the ease of having our North York office accessible for collaboration and/or quiet workspace.
Company Overview