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Open nowPosted 17 hours ago

Data Scientist - Commercial Analytics & Agentic AI

EURES job mobility portal92,923 open roles

Where
Agglomeratie ’s-Gravenhage, Netherlands
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Your applicationOpen nowData Scientist - Commercial Analytics & Agentic AIEURES job mobility portal · Agglomeratie ’s-Gravenhage, Netherlands

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on EURES job mobility portal's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

7.7% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 1.6%1 day
  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 17 hours ago

The posting

Finance Business Support aicarriere.nl Data Scientist - Commercial Analytics & Agentic AI 30-09-2026 6.378 - 8.504 Senior Den Haag Als Data Scientist - Commercial Analytics & Agentic AI bij NN vertaal je complexe commerciële data naar voorspellende inzichten, schaalbare analytics en agentic AI-use cases, en operationaliseer je dit in DAP en Salesforce voor betere salessturing. Direct solliciteren Neem contact op Data Scientist - Commercial Analytics & Agentic AI Role purpose: Build and deploy data-driven commercial analytics and agentic AI solutions that improve revenue performance, customer outcomes, and decision-making across sales, marketing, and pricing. Core responsibilities - Develop predictive and prescriptive models (e.g., propensity, churn, demand, pricing, segmentation) to support commercial strategy. - Design, prototype, and operationalize agentic AI workflows (tool-using LLM agents, orchestration, evaluation) for commercial use cases. - Translate business problems into measurable hypotheses, experiments, KPIs, and model success criteria. - Build robust data pipelines and feature engineering processes; ensure data quality and lineage. - Deploy models to production (APIs, batch scoring, dashboards) and monitor performance, drift, and business impact. - Partner with stakeholders to communicate insights, recommendations, and tradeoffs to technical and non-technical audiences. - Apply responsible AI practices: privacy, security, bias testing, and governance. Required skills - Analytics & modeling: statistical inference, experimentation, forecasting, ML (classification, regression, clustering), and model evaluation. - Programming: Python (pandas, scikit-learn) and SQL; experience with notebooks and version control. - LLMs & agents: prompt engineering, retrieval-augmented generation, agent frameworks, and automated evaluation. - Data & production: ETL/ELT, data warehouses/lakes, MLOps, CI/CD, monitoring. - B...

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