Principal Data Scientist

Responsibilities

• Lead AI engagements and be the main responsible for project delivery
• Manage key project stakeholders (technical and nontechnical) on project delivery and for future opportunities
• Prepare and lead software architecture and design discussions
• Review team design decisions and guarantee high quality code
• Navigate uncertain projects with poor scope and task definition, bring structure and organization
• Push for business impact and value delivery
• Bring expertise on Machine Learning AI agents to automate business workflows, propensity modelling to roll out marketing campaigns, forecasting for demand prediction and assortment optimization.
• Present to technical and or business stakeholders. Prepare and carry out presentations with the key business stakeholders involved in the projects to show the results of ML models to a non-technical audience and how they bring value to the business.
• Gather business requirements from business users and technically implement them to meet the requirements.
• Defining business requirements, document them, create wireframes, communicate to developers, monitor development, conduct internal testing and validate every KPI numbers & formulas.
• Own and develop relationship with partners, working with them to optimize and enhance our integration.
• Report on common sources of technical issues or questions and make recommendations to product team.
• Communicate key insights and findings to product team.
• Respond to ad‑hoc business analysis and conduct business analytics using complex SQL queries.

• 2+ years of demonstrable experience leading teams, being accountable for end‑to‑end project delivery and responsible for planning, assigning tasks and growing other team members.
• 4+ years of prior experience in a Data Scientist role as an IC.
• Demonstrable architecture and software design knowledge.
• Strong data engineering and software development skills.
• Expertise in Python and any one of the associated frameworks (scikit‑learn, Tensor Flow, PyTorch, h2o, etc.) for ML and DL.
• Expertise in the concepts of statistics, machine learning and agent‑based AI.
• Knowledge in ML‑Ops (deployment of models, model performance monitoring, data drift, REST API integration, simulations, etc.).
• At least 3 machine learning project implementation experiences in production.
• Excellent communication and collaboration skills, able to explain complex effects and impacts in simple business terms.
• Knowledge of optimization techniques (OR, MIP, …) is a plus.
• Knowledge on Data Iku is a plus.

• Be part of an international and talented team, enjoying an excellent work environment where people are passionate about what they do.
• Attractive salary according to experience and knowledge provided.

Or share with a friend