nShift

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. Purpose of Role We are creating a new senior role to partner directly with our Chief Customer Officer (CCO) in transforming a post-sales organisation of roughly 140 people across Support, Carrier Integration, and Professional Services, spanning multiple European geographies. This is not a coordination or reporting role. Given how dispersed the organisation is, this person's presence in our offices — running workshops, working alongside teams, building solutions on the ground — is itself part of how we bring the CCO's leadership closer to the frontline. The role exists to personally own and deliver our highest-priority transformation initiatives, not to track them. Please note that this is not a leadership role Overall Responsibility The (Senior) Director Of Post Sales Operations to the CCO will lead the hands-on delivery of transformation initiatives across the post-sales organisation, reporting to the Chief Customer Officer. This is an execution-first role: leading workshops, building AI-enabled tools and workflows, and driving initiatives from idea through to working reality, in close partnership with functional leaders across Support, Carrier Integration, and Professional Services. Frequent travel across our European offices is expected and central to how this role operates. Competence and Experience The area of responsibility includes: Hands-On Transformation Leadership * Personally lead and deliver the CCO's highest-priority transformation initiatives across Support, Carrier Integration, and Professional Services, from design through to execution. * Run and facilitate working sessions and workshops directly with functional leaders and frontline teams to design new ways of working. * Take initiatives from idea to built reality, working alongside the teams doing the work rather than directing from a distance. AI Fluency & Building * Bring genuine, hands-on AI fluency: able to build or prototype AI-enabled workflows, tools, or automations that remove friction from post-sales operations. * Identify where AI and automation can accelerate transformation across the three functions, and personally help stand up early solutions rather than only scoping them for others. Cross-Functional Execution * Translate the CCO's strategic priorities into concrete, deliverable initiatives that span more than one of the three functions. * Actively manage cross-functional handoffs and dependencies so initiatives land, not just get tracked. * Represent the CCO in forums and decisions where direct involvement isn't required. Frontline Presence & Stakeholder Management * Spend significant time in person across our European offices, bringing visible leadership presence to a highly dispersed team. * Build genuine, trusted working relationships with functional leaders and frontline staff across Support, Carrier Integration, and Professional Services. * Support internal talent development and act as a connector between frontline teams and the executive team. Key Requirements Experience & Background * 10+ years in senior post-sales, customer operations, or transformation leadership roles within B2B SaaS, ideally spanning more than one of: Support, Professional Services, Customer Success, or Partner/Integration functions. * A demonstrated track record of personally building things — an AI-enabled tool, an automated workflow, a from-scratch operating model, or similar — not just directing others to build them. * Experience leading hands-on transformation or operating-model change, including running workshops and driving execution to completion. * Comfortable operating in ambiguity, building structure where none exists yet, without waiting for a fully-formed mandate. * Experience navigating senior leadership environments and translating complex operational issues into clear, actionable decisions. Skills & Attributes * Genuine, hands-on AI fluency — comfortable building or prototyping with AI tools, not just directing others to. * Strong facilitation skills; able to run a workshop and drive a room to a decision. * Excellent written and verbal communication; able to distil complexity into clear, direct recommendations. * Fluent written and spoken English. Behaviours & Competencies * Builder mindset: prefers doing to directing, and delivering to reporting. * Structured, decisive, and comfortable operating in an evolving environment. * Genuinely willing to travel frequently across our European offices — this role is defined by frontline presence, not remote coordination. * Collaborative, low-ego partner to functional leaders across Support, Carrier Integration, and Professional Services. Why This Role Matters A post-sales organisation this dispersed doesn't transform through dashboards and steering committees alone — it transforms when someone senior enough to matter is in the room, building the solution alongside the team that has to live with it. This role brings that presence and that capability directly into the CCO's leadership, turning strategic priorities into things that actually get built. 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.

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: 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. 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. #LI-HS1 #LI-Hybrid