Product Manager, Data Insights

nShift; Aarhus; On-site; DKK 900,000 - 1,300,000; Full time

Job summary

nShift is seeking a product manager with a data science background to define and ship AI-powered intelligence for shippers. You will lead ML proof-of-concepts, data strategy, and the integration of data insights into existing products, collaborating across data, engineering, and commercial teams.

You will balance commercial impact with responsible data practices, including GDPR considerations, and articulate the defensibility of the data asset to enterprise buyers.

Qualifications

• Hands-on data proficiency across ML and data analysis using Python and standard ML tooling.
• Shipped ML-powered products with production deployment and commercial decision data.
• PM fundamentals with commercial depth.

Responsibilities

• Define and own data-driven product strategy and roadmap.
• Run ML proofs-of-concepts, feature engineering, and model evaluation.
• Partner with data, engineering and commercial teams to ship intelligence features.
• Govern data via anonymization and consent frameworks.

Skills

ML & data analysis

Python

Feature engineering

LLM pipelines

Tools

Python

scikit-learn

pandas

XGBoost

LLM tooling

Job description

About Us

nShift is the leading global provider of cloud delivery management solutions (SaaS), we enable the frictionless shipment and return of almost one billion shipments across 190 countries each year. We are headquartered in London and Oslo and have over 500 employees across offices in Sweden, Finland, Norway, Denmark, the United Kingdom, Poland, the Netherlands, Belgium, and Romania.

Our software is used by many of the world leading e-commerce, retail, manufacturing, and 3PL shippers due to us having over 1000 carriers integrated into our platform, nearly 3 times more than our competitors!

If you buy goods online, there is a strong chance that nShift has powered that delivery, so come and join us as we shape the future of shipping, one frictionless journey at a time.

About You

You think like a product manager and work like a data scientist. You have shipped ML-powered products to real customers and 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 insights is nShift's path from a tool company to an intelligence platform, and that scope motivates you rather than overwhelming you.

You are as comfortable in a Jupyter notebook as in a leadership meeting, and you know when each is needed.

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