Are 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.
We are looking for a Data Scientist 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.
You 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.
From 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.
As 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.
While 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.
Key Responsibilities
- Translate Supply Chain business challenges into data science problems and analytical frameworks.
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Develop predictive and prescriptive models (e.g., demand forecasting, inventory optimization, service level prediction, lead-time analysis).
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Perform exploratory data analysis and feature engineering using curated data products.
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Design, train, validate, and optimize machine learning models.
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Apply statistical techniques and experimentation methodologies to validate impact.
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Collaborate with Data Engineers to ensure data readiness, quality, and scalability.
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Support model deployment and monitoring in cloud environments.
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Ensure explainability, robustness, and governance of AI solutions.
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Quantify business impact through KPI definition and performance measurement.
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Communicate insights and model outcomes to both technical and non-technical audiences.
How does the ideal candidate look like:
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1–3 years in Data & AI projects (strategy and/or technical development), ideally with exposure to Supply Chain & Operations or related domains.
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Hands-on technical expertise in building AI solutions—experience with:
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Python (mandatory)
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Machine Learning libraries (scikit-learn, XGBoost, TensorFlow, PyTorch, or similar)
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SQL for data exploration
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Strong understanding of:
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Supervised and unsupervised learning
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Time series forecasting
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Optimization techniques (basic linear programming or heuristics is a plus)
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Model evaluation and validation frameworks
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Experience working with cloud-based environments (Azure ML, Databricks, or similar).
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Understanding of end-to-end AI lifecycle: experimentation, deployment, monitoring, governance.
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Ability to align analytical solutions with business KPIs and measurable value.
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Analytical mindset with strong problem-solving skills.
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Excellent communication skills, simplifying complex analytical concepts for diverse audiences.
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Adaptability and collaboration in global, fast-paced environments.
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Proficiency in English (required); additional languages are a plus.
The 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.
Technical Skills
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Programming: Python (mandatory), SQL.
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Machine Learning: Predictive modeling, feature engineering, model tuning and validation.
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Time Series & Forecasting: ARIMA, Prophet, ML-based forecasting methods.
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Optimization Techniques: Basic operations research or heuristic methods (plus).
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Data Visualization: Communicating insights through dashboards or visualization tools.
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Cloud & ML Platforms: Azure ML, Databricks, or similar.
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Model Governance: Monitoring, explainability, and performance tracking.
Strategic & Consulting Skills
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Problem Framing: Translating business challenges into analytical solutions.
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Data & AI Strategy Alignment: Ensuring models integrate within broader data ecosystems.
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Data & AI Strategy: Ability to define and execute strategies aligned with business objectives.
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Business Case Development: Quantifying impact, defining KPIs, and aligning with executive priorities.
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Critical Thinking & Problem Solving: Structured approach to complex challenges.
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Communication & Presentation: Simplifying technical complexity for diverse audiences.
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Adaptability: Thriving in global, fast-paced, and evolving environments.
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