ppAre you ready to design intelligent solutions that transform Supply Chain decision‑making? At Accenture, we are reinventing organizations through technology, data, and artificial intelligence—helping clients unlock new sources of value and measurable impact for their businesses and society. /ppWe are looking for a bData Scientist /b to join our global team. This role focuses on developing advanced analytics and AI models that leverage curated Supply Chain data products (e.g., Active Inventory, Demand, Shipments, Purchase Orders) to generate predictive insights and optimization capabilities. /ppYou will play a key role in translating complex business problems into data‑driven solutions—designing models that improve forecasting accuracy, optimize inventory levels, enhance service performance, and drive operational efficiency. /ppFrom exploratory data analysis to model deployment and monitoring, you will contribute across the full AI lifecycle—problem framing, feature engineering, model development, validation, deployment, and performance tracking. /ppAs part of our team, you will work on cutting‑edge initiatives that integrate Machine Learning, optimization techniques, and AI‑driven decision support systems—ensuring solutions are scalable, explainable, and aligned with enterprise data strategies. /ppWhile the role is deeply analytical and technical, you will also collaborate with data engineers, architects, and business stakeholders to ensure that analytical models translate into measurable business impact. /ph3Key Responsibilities /h3ulliTranslate Supply Chain business challenges into data science problems and analytical frameworks. /liliDevelop predictive and prescriptive models (e.g., demand forecasting, inventory optimization, service level prediction, lead‑time analysis). /liliPerform exploratory data analysis and feature engineering using curated data products. /liliDesign, train,



validate, and optimize machine learning models. /liliApply statistical techniques and experimentation methodologies to validate impact. /liliCollaborate with Data Engineers to ensure data readiness, quality, and scalability. /liliSupport model deployment and monitoring in cloud environments. /liliEnsure explainability, robustness, and governance of AI solutions. /liliQuantify business impact through KPI definition and performance measurement. /liliCommunicate insights and model outcomes to both technical and non‑technical audiences. /li /ulh3How does the idóneo candidate look like /h3ulli1–3 years in Data AI projects (strategy and/or technical development), ideally with exposure to Supply Chain Operations or related domains. /liliHands‑on technical expertise in building AI solutions—experience with:ulliPython (mandatory) /liliMachine Learning libraries (scikit‑learn, XGBoost, TensorFlow, PyTorch, or similar) /liliSQL for data exploration /li /ul /liliStrong understanding of:ulliSupervised and unsupervised learning /liliTime series forecasting /liliOptimization techniques (basic linear programming or heuristics is a plus) /liliModel evaluation and validation frameworks /li /ul /liliExperience working with cloud‑based environments (Azure ML, Databricks, or similar). /liliUnderstanding of end‑to‑end AI lifecycle: experimentation, deployment, monitoring, governance. /liliAbility to align analytical solutions with business KPIs and measurable value.



/liliAnalytical mindset with strong problem‑solving skills. /liliExcellent communication skills, simplifying complex analytical concepts for diverse audiences. /liliAdaptability and collaboration in global, fast‑paced environments. /liliProficiency in English (required); additional languages are a plus. /li /ulh3Technical Skills /h3ulliProgramming: Python (mandatory), SQL. /liliMachine Learning: Predictive modeling, feature engineering, model tuning and validation. /liliTime Series Forecasting: ARIMA, Prophet, ML‑based forecasting methods. /liliOptimization Techniques: Basic operations research or heuristic methods (plus). /liliData Visualization: Communicating insights through dashboards or visualization tools. /liliCloud ML Platforms: Azure ML, Databricks, or similar. /liliModel Governance: Monitoring, explainability, and performance tracking. /li /ulh3Strategic Consulting Skills /h3ulliProblem Framing: Translating business challenges into analytical solutions. /liliData AI Strategy Alignment: Ensuring models integrate within broader data ecosystems. /liliData AI Strategy: Ability to define and execute strategies aligned with business objectives. /liliBusiness Case Development: Quantifying impact, defining KPIs, and aligning with executive priorities. /liliCritical Thinking Problem Solving: Structured approach to complex challenges. /liliCommunication Presentation: Simplifying technical complexity for diverse audiences. /liliAdaptability: Thriving in global, fast‑paced, and evolving environments. /li /ulpThe position is based in Barcelona or Madrid and follows a hybrid work model, with some days working from home and others in the office, where you can create interesting synergies with the rest of your team. It is essential to reside in Spain and have a work permit in Spain. /p /p #J-18808-Ljbffr

📌 Data & AI Strategy Data Science Analyst (Madrid)
🏢 Accenture España
📍 Madrid