What You'll Be Doing
Lead and develop a high-performing team, fostering individual growth and collaboration.
Develop advanced analytical solutions while bridging the gap between data science and the engineering world: transforming complex data science concepts into practical, scalable solutions.
Build scalable ML infrastructure and pipelines for efficient data processing and model deployment. Evaluate architecture solutions based on cost, business needs, and emerging technologies.
Manage and mentor ML engineers, ensuring their professional development and effectiveness.
Monitor application health, set and track relevant metrics, and implement effective maintenance strategies.
Stay abreast of industry methodologies, explore new technologies, and champion their adoption within the team.
Advocate for improvements, scaling, and extension of ML tooling and infrastructure.
Foster a culture of innovation, collaboration, and excellence within the ML engineering team.
3+ years leading an ML team of at least 4 people in a fast-paced production environment
Relevant work or academic experience involved in the application of Machine Learning to solve business problems
University Degree in a quantitative field, MSc – advantage
Experience designing and executing end-to-end solutions for deploying different ML models
Experience with cloud frameworks like AWS sagemaker for training, evaluation and serving models
Experience with big data processing frameworks such, Pyspark, Apache Flink, Snowflake or similar frameworks
Good understanding of machine learning algorithms, statistical models, and data structures
Experience collaborating cross-functionally in the development of machine learning products (e.g., Developers, UX specialists, Product Managers, etc.)
Excellent English communication skills, both written and verbal
Successfully driving technical, business and people related initiatives that improve productivity, performance and quality while communicating with stakeholders at all levels
