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.
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.
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.
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.
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?
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.
What you'll bring
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.
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.
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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