We are looking for a Senior Data Scientist to join a global retail and consumer goods company. This professional will be responsible for leading Machine Learning and Analytics initiatives, from identifying business opportunities to deploying and supporting production-ready solutions.

Key Responsibilities

Discovery & Solution Definition

• Understand business challenges and translate them into analytical and technical hypotheses.
• Partner with Product Owners and stakeholders to define data and AI products.
• Evaluate the most suitable solution consumption model (real-time, batch, dashboards, APIs, or insights).
• Support the definition of business rules and requirements for Machine Learning products.

Development & Deployment

• Perform exploratory data analysis (EDA) and validate hypotheses.
• Design and develop Machine Learning models and experimentation pipelines.
• Define data architecture, algorithms, and product evolution roadmaps.
• Implement, test, optimize, and review model and pipeline code.
• Establish deployment, orchestration, and monitoring processes for production models.
• Support solution maintenance and continuous product improvement.

Communication & Results Management

• Present results and recommendations to business stakeholders and leadership teams.
• Define success metrics, benchmarks, and performance indicators.
• Gather and incorporate stakeholder feedback to continuously improve solutions.

Technical Leadership

• Lead technical discussions and prioritize initiatives.
• Mentor data scientists and support the team's technical development.
• Promote best practices in development, governance, and documentation.

Business Partnership

• Translate technical outcomes into measurable business impact.
• Collaborate closely with Product, Engineering, Operations, and Commercial teams.
• Stay current with market trends, innovation, and emerging technologies applicable to the business.

Requirements

Must-Have Qualifications

• Strong experience in Data Science, Machine Learning, or related fields.
• Strong proficiency in Python or R and Machine Learning libraries/frameworks.
• Solid knowledge of statistical modeling, experimentation, and data analysis.
• Experience developing and deploying Machine Learning models into production environments.
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
• MLOps concepts and model monitoring, version control, and pipeline orchestration.
• Strong SQL skills and experience working with large-scale datasets.
• Excellent communication skills, with the ability to interact effectively with both technical and business stakeholders.
• Analytical mindset, consultative approach, and results-oriented attitude.

Nice-to-Have Qualifications

• Experience with Databricks, MLflow, Lakehouse architectures, or similar platforms.
• Knowledge of Generative AI, Large Language Models (LLMs), and RAG architectures.
• Experience in retail, consumer goods, or other large-scale data-driven environments.
• Previous experience providing technical leadership and mentoring team members.