Description :

The Core AI and Optimization Team is the engine driving high-impact analytical transformations across Finance, Manufacturing, and Digital Platforms. As a team of data scientists and product experts embedded in IT, we deliver measurable value by turning complex business problems into deployable, production-ready AI, ML, and optimization solutions.

Your Impact :

As a Data Scientist on this team, your primary impact will be to design, build, and deploy the algorithms that underpin our core transformation initiatives, ensuring speed, accuracy, and operational stability. You will :

- Pioneer Core AI Capabilities : Develop and industrialize Core AI models (including predictive and prescriptive analytics) to drive measurable improvements in business processes and operational efficiency

- Design Optimization Logic : Architect and implement Optimization models (e.g., linear programming, heuristics, simulation) to solve constrained resource allocation problems within our manufacturing environments.

- Bridge Science to Business : Partner directly with Business Analysts and business stakeholders to translate strategic questions into testable hypotheses and deployable ML/Optimization features, clearly communicating model performance and business value.

- Production Deployment & Maintenance : Own the technical integrity of the models, focusing on data pipeline integration, MLOps, scalability, and integration into new Digital Platforms.

What You Will Do :

I. Model Development & Research :

- End-to-End Modelling : Lead the full lifecycle of model development : from data sourcing and feature engineering to algorithm selection (ML, Deep Learning, Optimization), training, validation, and documentation.

- Applied Optimization : Implement and solve high-dimensional optimization problems, leveraging solvers and appropriate techniques to inform real-time business decisions.

- Code & Architecture : Write production-quality, highly efficient, and maintainable code zfor model implementation, ensuring best practices in version control and testing.

II. Data Engineering & MLOps :

- Data Pipeline Integration : Collaborate with data engineering teams to establish robust, scalable data ingestion and feature pipelines, ensuring data quality and availability for training and inference.

- MLOps Implementation : Be accountable for the deployment and continuous monitoring of models in a production environment, implementing MLOps best practices for drift detection and retraining.

- Visualization & Diagnostics : Develop clear, insightful data visualizations and model diagnostics to communicate performance, explainability, and potential biases to technical and non-technical audiences.

III. Collaboration & Engagement :

- Technical Consulting : Act as a technical expert to consulting teams, advising on the feasibility and best analytical approach for new transformation use cases across Operations, Finance, and Manufacturing.

- Knowledge Contribution : Contribute to the firm's central knowledge base by codifying and documenting reusable model architectures, feature libraries, and best-in-class MLOps frameworks.

Qualifications & Skills :

Required Technical & Analytical Expertise :

- Experience : 4-6 years of professional experience as a Data Scientist, Applied Scientist, or Quantitative Analyst, with a focus on building and deploying production-grade solutions.

- Education : Bachelors degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Operations Research, Engineering). A Masters degree is preferred.

- Core Programming : Expert proficiency in Python and data science ecosystem.

- ML & Optimization : Expertise in Machine Learning models, Mathematical Optimization applied to real-world resource allocation problems.

o Data architectures & MLOps : Proficiency in SQL for complex data querying and manipulation. Experience with MLOps principles and tools

Domain Skills :

- Domain Expertise : Proven experience developing analytical solutions for Industry/Manufacturing across business functions like finance, logistics, utilities etc.

- Cloud Platforms : Working knowledge of major cloud services for data processing and model deployment (AWS, Azure, or GCP).

- Communication : Proven ability to synthesize complex modelling results into clear, concise business narratives and presentations for non-technical leadership.