Original listing text, shown exactly as published by the company.
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.…
