Product Manager, Data Insights

nShift

Product Manager for Data Insights at nShift, shaping data strategies for cloud delivery solutions. Collaborating with teams to drive product functionality and data integration across various sectors.

Posted 6/18/2026full-timeStockholm • 🇸🇪 SwedenMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

PandasPythonScikit-Learn

About the role

Key responsibilities & impact

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)
Build and validate before engineering commits

Requirements

What you’ll need

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)
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
Data literacy that earns engineering respect: you understand data models, pipeline design, normalization challenges, and the practical constraints of real production data
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.

Benefits

Comp & perks

Flexible work arrangements
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdata explorationfeature engineeringmodel trainingmodel evaluationdata modelingpipeline designnormalizationB2B SaaS product developmentdata intelligence

Soft Skills

clear communicationcollaborationprioritizationdecision-makingwritten communicationverbal communicationstrategic thinkingcross-functional teamworkproblem-solvingadaptability