Purpose of role

The mission is to define what nShift builds with that data, validate it before engineering commits, and ship intelligence products that help shippers make better decisions. A shipper asking "how can I improve my delivery experience" should get an answer powered by the full network, 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.

The role sits at the intersection of product management and data science. You have the technical depth to build ML proof-of-concepts independently and the commercial judgment to translate those capabilities into products customers pay for. You write the strategy and the first notebook.

What you'll be doing

Define and own the data driven product strategy

• Own the roadmap for nShift's data intelligence layer: carrier performance prediction, cross-customer benchmarking, ML-driven carrier recommendations, and related capabilities.
• 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.

Build and validate before engineering commits

• Run ML proof-of-concepts independently: data exploration, feature engineering, model selection, and initial validation, using Python, notebooks, and the data available in nShift's data layer.
• Define the evaluation framework for both ML models and LLM pipelines. For ML features: what accuracy thresholds matter, how to measure model 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 insights capabilities into existing products. Work closly 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.

Govern the data responsibly

• Define the anonymization, aggregation, and consent frameworks that make cross-customer benchmarking legally and ethically sound, and credible to enterprise customers who will ask hard questions.
• Work with Legal and the DPO to ensure data usage within the intelligence layer meets GDPR and contractual obligations. The data asset is only valuable if customers trust nShift to use it appropriately.
• Establish transparency standards for ML-powered recommendations: when does a merchant see why a carrier was recommended? What level of explainability builds trust without revealing proprietary network data?

What you'll bring

Must-have

• Hands-on data proficiency across both paradigms: you can build and validate ML proof-of-concepts (data exploration, feature engineering, model training, evaluation) using Python and standard ML tooling (scikit-learn, pandas, XGBoost, or equivalent), and you can design and evaluate LLM-based analysis pipelines (prompt design, retrieval-augmented approaches, output quality evaluation). You are not a data scientist or an ML engineer, but you can work like one when the problem requires it.
• Shipped ML-powered products: you have personally driven an ML or data intelligence product from initial hypothesis to production, including defining evaluation criteria, partnering with data engineers and scientists, and making commercial decisions based on model performance data.
• 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. You can review a data schema and immediately ask the right questions.
• 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 modelling, routing optimization, ETA prediction, or related domains.
• Familiarity with data platform concepts: data lake houses, feature stores, model registries, observability for ML in production.
• Background in cross-customer benchmarking or network intelligence products, where the value comes from aggregation rather than individual customer data.
• 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 product at a company, defining standards rather than inheriting them.