AI Translator

Milacron Talent Acquisition•Batavia, OH

1d•Hybrid

About The Position

Responsible for bridging Milacron's business operations and the enterprise AI platform within an assigned business unit (Supply Chain, HR, Finance, Operations, Engineering, Sales, Aftermarket, or India). The AI Translator is a credible business operator — not a machine learning engineer — who translates business problems into AI use cases, drives model adoption on the ground, owns the EBITDA outcomes associated with the BU's AI initiatives, and absorbs capability from external system integrators so the business can run independently. This role is the connective tissue between the Central AI / Data Center of Excellence and the business unit it serves, and is expected to deliver from problem identification to first pilot within 60–90 days under Milacron's embedded hybrid AI operating model.

Requirements

Bachelor's degree in business, engineering, operations, analytics, computer science, or a related discipline; advanced degree preferred but not required
7+ years of progressive experience in operations, business management, or functional leadership within a manufacturing, industrial, or capital equipment environment
Demonstrated track record of owning and delivering measurable P&L outcomes (revenue, cost, margin, working capital, or service-level performance)
Working fluency with AI / ML concepts, generative AI applications, and modern data platforms — sufficient to scope use cases, evaluate vendor proposals, and challenge model assumptions, without requiring hands-on model development
Strong stakeholder management skills, with proven ability to influence senior business leaders, technical teams, and external partners across multiple time zones
Experience leading change management and digital adoption programs in environments with mixed levels of technical comfort and digital maturity
Proficient with Microsoft 365, Salesforce (SFDC), PowerBI or equivalent BI tools, and modern collaboration platforms
Excellent written and verbal communication skills, with the ability to move fluidly between technical and non-technical audiences and between front-line operators and executive leadership
Sound judgment on data quality, model risk, and the limits of AI — and the willingness to push back on use cases that do not meet the bar for business value or responsible deployment

Nice To Haves

Prior experience working alongside system integrators or consulting partners and successfully transitioning capability in-house is strongly preferred
Plastics processing, capital equipment, aftermarket service, or industrial manufacturing domain knowledge is strongly preferred
An ML engineering, data science, or PhD background is not required and is not a substitute for business operating experience
Prior experience with Palantir Foundry and/or Ontology is strongly preferred.

Responsibilities

Translate business problems into structured AI use cases for the Central AI / Data CoE, and translate model outputs, assumptions, and constraints back into business language for the BU leadership team
Maintain deep BU-specific fluency in workflows, KPIs, systems of record, and the underlying data sources owned by the assigned business unit
Own change management on the ground — train, coach, and earn user trust so AI adoption sticks beyond pilot and becomes the default way of working
Hold accountability for the EBITDA KPIs assigned to the BU's AI portfolio (e.g., revenue uplift, cost reduction, cycle-time improvement, working-capital release)
Govern AI deployments within the BU by flagging model risk, data quality issues, biased or unintended outputs, and escalating appropriately to the CoE governance function
Absorb capability from system integrators and external vendors so the BU can sustain, extend, and eventually evolve AI solutions without ongoing third-party dependency
Partner with Central AI / Data CoE platform engineers and ML developers to prioritize the backlog, define data contracts, and validate model performance against business outcomes
Lead use-case discovery, business case development, and post-deployment ROI measurement for AI initiatives within the BU, in alignment with enterprise investment guardrails
Coordinate data preparation, system integration, and pilot rollouts with internal IT, data engineering, and external delivery partners
Champion responsible AI practices including data privacy, model explainability, human-in-the-loop controls, and clear audit trails for AI-influenced decisions
Represent the BU in enterprise AI governance forums and contribute to the company-wide AI roadmap and investment prioritization
Travel domestically and occasionally internationally to BU sites, customer facilities, and partner / vendor locations as required to observe workflows and drive adoption
Other AI-related activities as requested by the Senior Director, AI & Engineering or the Operating Leadership Team