About you

You are a Product Manager who has treated data as the product. You have shipped insights, benchmarks, or recommendations to external customers, ideally built on data aggregated across many of them, and you can point to the decisions you made, not just the ones made around you. Messy data, ambiguous model outputs, and stakeholders who don't yet see why the data matters are familiar territory.

You think in terms of compounding: how every data point collected today improves predictions tomorrow, and how that improvement builds a position competitors cannot close. Data-driven products are nShift's path from a tool company to an intelligence platform, and that scope motivates you rather than overwhelming you.

You are as comfortable reviewing a model's results with an ML Engineer as you are presenting the product case in a leadership meeting, and you know when each is needed. You may have come to product from a data science or ML background; what matters is that you now think in terms of customers, value, and decisions, not just models.

Purpose of role

nShift sits on one of the richest delivery datasets in the industry: close to a billion shipments a year, across more than 1,000 carriers and thousands of shippers. The mission of this role is to turn that data into the product itself: to define what nShift builds with it, validate it before engineering commits, and ship data-driven products that help shippers make better decisions. A shipper asking "how can I improve my delivery experience" should get an answer powered by the aggregated experience of the whole nShift community, not just their own history. Done well, that becomes a compounding intelligence layer: every data point collected today improves suggestions tomorrow, and the improvement creates a position no competitor can close.

The intelligence layer draws from two sources. Structured operational data, such as shipment telemetry and carrier performance, feeds statistical models and predictions. Qualitative data, such as customer support interactions, delivery exception messages, and community feedback, carries signal that numbers alone cannot surface. LLMs make that signal extractable and usable at scale. This role owns both.

This is a product role first. You have built products where data is the thing being sold: insights, benchmarks, predictions, or recommendations served to external users, not internal dashboards. You are technically fluent enough to work with data scientists and ML engineers as a peer, shape the hypotheses they test, and judge whether the results justify a product. You will be paired with an ML Engineer from day one, so you don't need to build the models yourself; you need to know what to build, why customers will pay for it, and when the data says to stop.

What you'll be doing

Define and own the data driven product strategy

• Own the roadmap for nShift's data-driven products: cross-customer benchmarking, carrier performance prediction, ML-driven carrier recommendations, and related capabilities that turn aggregated network data into insights shippers act on.

• Translate nShift's data asset into a sequenced product strategy that delivers near-term value while building toward the compounding intelligence layer vision.

• Define the information architecture for the data insights layer: what data is collected, how it is normalized (in partnership with adjacent teams), and how it is exposed to upstream products.

• Make hard prioritization calls: what to build first to validate the ML hypothesis, what to defer, and what to kill when the data doesn't support the bet.

• Own the qualitative intelligence pipeline alongside the quantitative one: define which unstructured sources carry decision-relevant signal (support tickets, delivery exception messages, customer forum, carrier communications), how LLM-extracted insights are structured for downstream use, and how qualitative and quantitative signals combine into a coherent intelligence product.

• Own the positioning and differentiation narrative: why this dataset is uniquely nShift's, why no competitor can replicate it, and how to articulate that defensibility to enterprise buyers.

What you'll bring

Must-have

• Experience with data as the product: you have shipped a product whose core value was insights, benchmarks, predictions, or recommendations served to external customers. Ideally that product drew on data aggregated across many customers, where the value came from the network rather than any single account. Internal BI, reporting, or dashboard work on its own is not what we are looking for.

• Shipped ML- or AI-powered features: you have driven at least one ML- or AI-based capability from hypothesis to production, including defining what success looked like, partnering with data scientists and engineers, and making commercial decisions based on how the model actually performed.

• Technical fluency without needing to build: you understand how ML models and LLM pipelines work well enough to shape hypotheses, challenge evaluation results, and read a notebook or a schema and ask the right questions. You do not need to train the models yourself; you need to be a credible partner to the people who do.

• PM fundamentals with commercial depth: 3+ years shipping B2B SaaS products. You can build a business case, define pricing, run customer discovery, and hold direct conversations with enterprise buyers about ROI.

• Data literacy that earns engineering respect: you understand data models, pipeline design, normalization challenges, and the practical constraints of real production data, and you can hold your own in a design discussion with the engineers who build them.

• Clear communication across the technical and commercial boundary: you can explain a precision-recall tradeoff to a CPO and a carrier event data use case to a data engineer in the same afternoon, without condescending to either.

• Excellent written and verbal English: you will write strategy documents, ML experiment briefs, customer-facing product narratives, and present to leadership, often in the same week.

Nice to have

• Experience with time-series or logistics domain data: carrier performance modeling, routing optimization, ETA prediction, or related domains.

• Familiarity with data platform concepts: data lakehouses, feature stores, model registries, observability for ML in production.

• Hands-on ability to run your own data exploration or ML proof-of-concepts in Python (pandas, scikit-learn, or equivalent), or to prototype LLM-based analysis pipelines. Useful, not required: you will be paired with an ML Engineer for this.

• Experience with GDPR-compliant data product design in a multi-tenant B2B SaaS context.

• Nordic or European logistics market experience: carrier landscape, regulatory environment, freight economics.

• Track record of building the first data-driven product at a company, defining standards rather than inheriting them.

• A background as a data scientist or ML engineer before moving into product management.

Validate before engineering commits

• Frame and drive proof-of-concepts with your paired ML Engineer: define the hypothesis, the data needed, and what "good enough" looks like, then dig into the exploration and results alongside them rather than waiting for a readout.

• Own the evaluation criteria for both ML models and LLM pipelines, working with the team on the metrics themselves. For ML features: what accuracy thresholds matter to the customer, how to measure performance in production, how to distinguish signal from noise. For LLM-based features: how to assess output consistency, factual grounding, and quality at scale, where traditional accuracy metrics don't apply.

• Design lightweight experiments that answer the key questions before a full engineering team is committed. Does the prediction accuracy justify the product? Does cross-customer data improve the model significantly? Is the data we have sufficient, or do we need to instrument more?

• Translate POC findings into clear build, kill, or iterate decisions backed by data.

Partner across data, engineering, and commercial teams

• Work closely with engineers to define the data models, pipelines, and normalization standards needed to power ML features. You are a customer and co-designer of the data layer, not a passenger.

• Drive integration of data-driven capabilities into existing products. Work closely with your PM peers to make this happen.

• Engage directly with customers and prospects across the full spectrum, from small shippers to global enterprises, to validate intelligence use cases: what decisions do they make today that better data would improve? What would they pay for? What do they trust?

• Build the commercial narrative: ROI frameworks, value quantification, and positioning for the target buyer.

Please ensure you upload your CV in English

At nShift we believe in embracing diversity in all forms and fostering an inclusive environment for everyone which we believe is essential for our continued success. We're an equal-opportunity employer which means that all applicants will receive consideration for employment without regard to ethnicity, religion, sexual orientation, gender identity, family or parental status, national origin, veteran, neurodiversity, or disability status.

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