Staff Data Scientist
Bitso
- Ciudad de México
- Permanente
- Tiempo completo
- Have a degree in a quantitative field such as mathematics, data science, actuarial science or economics.
- An advanced degree (Masters or Ph.D.) in Mathematics, Computer Science, Data Science, or Machine Learning and 3+ years of industry experience or 5+ years of experience as a data scientist.
- A proven track record of machine learning solutions that translated into value.
- Experience in both supervised and unsupervised modeling.
- Strong proficiency in SQL.
- Strong proficiency in R or python.
- Understanding of big data manipulation tools such as Spark or Koalas.
- Strong ability to stay current on the latest advancements of machine learning and AI.
- Understanding of software engineering best practices, including version control, code reviews and automatic testing.
- Strong communication skills and the ability to convey complex technical concepts to both technical and non-technical stakeholders.
- Understanding of quantitative finance techniques such as time series analysis and extreme value theory.
- Understanding of monte carlo and markov chain techniques
- MLOps best practices experience.
- Databricks experience.
- Experience with GenAi modeling flows.
- Lead data science projects: find new opportunities for projects where machine learning might move the needle.
- Analyze large datasets to find customer segments, definitions or hidden opportunities.
- Design end to end machine learning solutions: create classic machine learning models and GenAi models that are production ready.
- Collaborate with non technical stakeholders to arrive at the best solution to given problems/opportunities.
- Maintain and create new versions of current machine learning models in production
- Contribute to the machine learning infrastructure alongside other data scientists and machine learning engineers.
- Monitor and troubleshoot production ML deployments, identifying and resolving issues in a timely manner.
- Stay up to date on advancements in ML technologies, MLOps, LLMOps, sharing knowledge within the team and contributing to skill development of other data scientists.