ShipMonk is hiring a Manager, Data & AI to shape and lead a new team inside Product Development. The team is roughly defined — senior data scientists and data engineers focused on transportation (Virtual Carrier Network optimization, predictive routing, claims) and IMS (Inventory Management System) data products, plus one AI Consultant who works horizontally across ShipMonk to identify and ship AI use cases — and we are actively hiring all of these roles now. Some hires may already be in the pipeline or signed by the time you start. Your job is to shape who actually lands in each seat, onboard them well, find the team's place in the org, and then make sure models reach production, savings show up in the P&L, and insights become product features merchants actually use.

 

You are an active, daily user of modern AI tools and you think about AI as a strategic lever, not a feature. You will set the AI agenda for the team and for ShipMonk's data function. The AI Consultant reports to you because you are the person who decides what's worth building, what's hype, and where AI changes the economics of how the company runs. Candidates who treat AI as someone else's job, or as a buzzword to put on a slide, are not the right fit for this role.

 

We hire Managers first and foremost for ownership, delivery, and the ability to turn a fresh team into a compounding one. Technical credibility is the floor — you must be sharp enough to interview senior data scientists and engineers, sit in design reviews, and earn respect from the people you hire — but we are not looking for the strongest IC in the room. We are looking for a builder who is fluent in AI, moves fast on hiring, onboards people into a clear mission, and turns a new team into a serious operating unit inside two or three quarters.

About the Role

You will report to the Director of Data Platform and partner directly with Product, Operations, CX, and Finance to shape the team's mandate and unblock delivery. The first version of the team is roughly five to seven people: a mix of senior data scientists and data engineers covering transportation and IMS, plus one AI Consultant. Hiring is happening in parallel with your own onboarding — depending on timing, you may join some hires already in the pipeline and others still to be opened. You will own the bar, the interview loop, and the final calls from the moment you start.

 

This role is a clear growth platform. With the right results, the natural growth path is Senior Manager or Director of Data, with broader scope across analytics, ML platform, and AI.

The Technical Environment

You don't need to be the deepest expert in our stack, but you will be expected to be conversant in it, run sharp technical interviews, and pair on hard problems when the team needs depth.

 

• Languages & tooling: Python, SQL, dbt.
• Warehouse & infra: Snowflake (or comparable cloud warehouse), AWS, Kubernetes, Argo CD.
• ML & data: model training and serving in production, feature pipelines, monitoring.
• Workflow: GitLab CI, code review, observability via DataDog and Sentry.
• AI tooling: Claude Code, Cursor, Copilot, and our internal Claude skills are part of how the team will work from day one.

Key Responsibilities

Shape the Team and Make It Real

The roles are defined and hiring is moving. Your job is to make sure the right people land in the right seats, onboard well, and become a team rather than five individual hires.

 

• Step into the live hiring loop fast. Calibrate the bar, run final-round interviews personally, and own the close on every offer from your first week.
• Decide which open seat each strong candidate fits into. The roles are roughly scoped, not rigid — part of your job is matching the actual people you hire to where they will do the best work.
• Onboard deliberately. Each new hire should know inside the first two weeks what they own, what success looks like in 90 days, and who their key partners are in Product, Operations, and CX.
• Hire the AI Consultant with extra care. This is not a junior role and not a generic ML engineer. You are looking for someone who can walk into a CX or Ops meeting, find a real problem, and ship something useful within weeks.
• Every new req, and every existing req in flight, carries a documented ROI justification tied to a specific project. You take ownership of this from day one: validate what is already in the pipeline, kill what does not hold up, and orchestrate new reqs cleanly with the PM, Finance, and Recruiting.
• Sell ShipMonk and the team externally. Show up in the Prague data community, write, speak, and be visible. You are a hiring magnet, not just a hiring manager.

Find the Team's Place in the Org

A new team inside an existing engineering org does not have its place by default. You have to define it.

 

• Establish how the team works with Platform on shared infrastructure, with Product on roadmap intake, with Operations and CX on use-case discovery, and with Finance on capitalization and ROI reporting.
• Define what the team owns, what it consults on, and what it explicitly does not do. Write it down.
• Build the relationships that make the above work. The first six months of stakeholder trust are disproportionately important.

Set Direction and Translate Strategy into Delivery

• Take the company strategy and the SVP of Engineering's priorities and turn them into a concrete v1 roadmap and OKRs. Predictive routing and VCN optimization, IMS data products, and the AI Consultant's use cases all compete for the same team capacity from day one. Make the trade-offs explicit and defensible.
• Run a transparent quarterly OKR cycle: set, communicate, review, course-correct. Surface what you killed, not just what you shipped.
• Own the cross-team contract with Product, Operations, CX, and Finance. When dependencies slip, escalate early, in writing, with options.

Set the AI Agenda

You own the AI agenda for the data function. The AI Consultant works for you, but the strategy comes from you.

 

• Decide where AI changes the economics of how ShipMonk runs and where it doesn't. Cut through hype.
• Define the intake process: how teams across CX, Operations, and Product request help, how you triage, how you decide what's worth doing.
• Co-own the AI roadmap with Product and Operations. Push back when a request is a feature in disguise or a science project with no business case.
• Make the wins visible, measurable, and reusable. One-off automations are fine; repeatable patterns are better.
• Drive AI-assisted development across the team. Claude Code, Cursor, Copilot, and our internal Claude skills are real productivity levers, not slogans. Set expectations, share what works, remove friction. You lead by using these tools yourself, daily.

Own Delivery, Quality, and Production

• Be accountable for what your team ships, when it ships, and how it behaves in production. Models that drift, pipelines that fail silently, and dashboards no one trusts are all your problem.
• Define and track the metrics that matter from the start: model performance in production, pipeline reliability, time from idea to deployed model, business impact per project.
• Set engineering standards (code review, testing, observability, documentation) early. Standards are easier to set on day one than to retrofit on day 300.
• When there is a major incident in your area, you own the response, the postmortem, and the systemic preventive action. Change the system, never blame the person.

Run a Business-Oriented Data Team

• Know your domain's economics. Be able to state, in plain language, how transportation data products affect routing margin and how IMS data products affect placement cost and stockouts.
• Frame technical investment in business terms: margin gained, cost avoided, support tickets reduced, revenue protected. "Better model accuracy" alone is not a justification at this level.
• Own quarterly ROI reporting to leadership against each project's business case. Move the team from "insights on a slide" to "logic in the code, dollars on the page."
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