Bachelor’s, Master’s, or PhD degree in Computer Science, Machine Learning, Applied Statistics, Mathematics, Engineering, Physics, or other technical fields
Up to 2 years of professional experience applying machine learning and data mining techniques to real-world problems with substantial data sets
Programming experience (focus on machine learning): R and/or Python, with SPSS, SAS, or similar tools considered nice-to-have
Ability to prototype statistical analysis and modeling algorithms and apply them to develop data-driven solutions in new domains
Ability to independently own and drive model development while balancing competing demands and deadlines
Demonstrated aptitude for analytics and a passion for solving complex data challenges
While we advocate using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, Kedro (our open-source data pipelining framework), Dask/RAPIDS, Docker, Kubernetes, and cloud solutions such as AWS, GCP, and Azure
Experience with Generative AI (GenAI) and agentic systems would be considered a strong plus
Excellent time management and organizational skills to succeed in a complex and largely autonomous work environment
Good presentation and communication skills, with the ability to explain complex analytical concepts to people from other fields
Willingness to travel
Strong communication skills, both verbal and written, in English, with the ability to adapt to different audiences and seniority levels
