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At Wiremind, the Data Science team is responsible for the development, monitoring and evolution of all ML-powered forecasting and optimization algorithms in use in our Revenue Management systems. Our algorithms are divided in 2 parts: A modelling of the unconstrained demand using ML models (e.g. deep learning, boosted trees) trained on historical data in the form of time-series; Constrained optimizations problems solved using linear programming techniques. You will be joining a team shaped to have all profiles necessary to constitute an autonomous department (data engineering, MLOps, ML, R&D). Within this organization, the ML team is responsible for the development, improvement and maintenance of new features and algorithms for our SaaS applications, using a mixture of proven traditional methods as well as recent breakthroughs in Deep Learning for regression problems.
Job Responsibility:
Lead, mentor and grow the ML team, composed of members with various experience levels, ensuring high-quality delivery and team cohesion
Participate in shaping the ML roadmap, and be responsible for turning it into an actionable and achievable plan for the team
Drive execution and delivery: structure the team's work, ensure priorities are clear, coordinate efforts across stakeholders, and remove blockers
Collaborate closely with R&D, Product, and Data Engineering to translate business needs into scalable ML features
Oversee new client implementations, ensuring a smooth and robust integration of algorithmic components into our SaaS products
Ensure the reliability of our models in production, supervising monitoring, debugging, and continuous improvement efforts
Requirements:
Master’s Degree in Data Science, Applied Mathematics or Computer Science with a minimum of 5 years of working experience
Strong technical background and master the full lifecycle of a data-science project
Familiar with MLOps concepts
2+ years experience in a management position
Result oriented and have a pragmatic approach to ML: testing and frequent deliveries of small incremental gains is preferred to a long tunneled research process
Enjoy mentoring junior colleagues, guiding them in their professional growth
What we offer:
Training on demand
A hybrid policy: 2 days of remote work per week and the possibility to work occasionally from abroad
A great company culture (monthly afterworks, regular meetings on technology and products, annual off-site seminars, team-building…)
An annual budget for your IT equipment
A partnership with the People & Baby network of inter-company nurseries to help with childcare for children aged 0 to 3