Senior Data Scientist

Some of the exciting projects you will work on include:

• Driving the design, rapid experimentation, development, testing and deployment of data science models for flow forecast models and anomaly detection;
• Optimising and fine-tuning models in production, overseeing the continuous monitoring of deployed models, and effectively handling model and data drift promptly;
• Building robust pipelines for integrating data from diverse sources, including big geospatial data, ship mobility data, and document recognition;
• Researching and identifying methods, data sources, and features that will drive business impact and improve models’ accuracy in the current scope of ever-changing world-scale commodity trading;

As a senior data scientist, you will:

• Devise efficient solutions to tackle ML/Big Data challenges using relevant, up-to-date methods and technologies.
• Work across the stack to deliver new features end-to-end, from prototyping, to deployment and caring for data drift in production.
• Ensure optimal, cost-effective design decisions that improve performance and overcome scalability limits.
• Own meaningful parts of our service, demonstrating the ability to lead projects independently, have an impact, and grow with the company.
• Identify opportunities for novel projects and liaise with product teams to advance ideas into value-adding features.
• Actively share knowledge and document insights, effectively communicate complex concepts and analysis to technical and non-technical audiences, aiming to support continuous team improvement and drive collaboration.
• Act as a mentor for our junior data scientists, helping to accelerate their growth; you will act as the Tech Lead on some projects.
• Be part of a vibrant Machine Learning community in Kpler, tackling the whole spectrum of ML problems.

Our Machine Learning tech stack includes:

• Python, ML libraries (TensorFlow, pytorch, scikit-learn, transformers, XGBoost, ResNet), geospatial libraries (shapely, geopandas, rasterio), AWS, Postgres, Apache Airflow, Apache kafka, Apache Spark.

Mandatory requirements:

• You have at least 5 years of experience in the DS role, deploying models into production;
• You have proven experience delivering end-to-end ML solutions that produce business value.
• You are proficient in Python.
• You have expert knowledge of at least one cloud computing platform (preferably AWS).
• You are fluent in English.

Nice to haves but not mandatory:

• You have expertise on applications focusing on geospatial data and mobility analytics (highly desirable).
• You have proven experience with big data technologies, specifically Spark and Kafka.
• You have experience working with state-of-art ML pipeline technologies (such as MLflow, Sagemaker...) or building a ML pipeline by yourself (Docker, Kubernetes, Paperspace, Airflow...).
• You have a Ph. D. in a quantitative field (computer science, mathematics, physics, engineering...).
• You are familiar with the shipping industry and commodity trading.
• You are comfortable with software engineering best practices.
• You value code simplicity, performance and attention to detail.
• You have experience working in an international environment.