Job Opportunities in Costa Rica

What you’ll do

• Research innovative data solutions from unstructured sources to solve real business problems.
  
• Design, implement, and maintain scalable data pipelines for data ingestion, transformation, and delivery to ML models.
  
• Develop and deploy advanced machine learning models using techniques like natural language processing, and computer vision.
  
• Collaborate with a team of engineers, data scientists, and product managers to deliver impactful AI solutions.
  
• Develop analytical approaches to meet business requirements; this involves translating requests into use cases, test cases, preparation of training data sets and iterative algorithm development
  
• Implement monitoring and logging solutions to track model performance and detect anomalies
  
• Remain current on new developments in data analytics, Big Data, predictive analytics, and technology.
  

What experience you need

• Bachelor’s Degree in Statistics, Mathematics, Computer Science (preferably) or equivalent to experience
  
• At least 4 years of experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark
  
• At least 4 years of experience coding with several programming languages: Python, Java, SAS, R
  
• At least 4 years of experience in developing and deploying machine learning algorithms in production , creating and using advanced Machine Learning algorithms: including supervised, unsupervised, and reinforcement learning techniques.
  
• At least 4 years of experience in data science and statistics : understanding of data cleaning, preprocessing, feature engineering, and statistical analysis and using popular frameworks such as Tensor Flow, Py Torch or scikit-learn
  
• 2+ years of experience working with cloud platforms (e.g. GCP, AWS, Azure) for deploying and managing ML systems .
  

What could set you apart

• GCP, AWS or Azure cloud certifications
  
• Experience with large language models (LLMs) and their applications in various domains.
  
• Knowledge of prompt engineering, fine-tuning, and evaluation techniques for LLMs.
  
• Familiarity with NLP tasks such as text classification, sentiment analysis, or data extraction.
  
• Experience with Agile development methodologies (SCRUM, XP, etc)
  
• Strong communication and collaboration skills: Ability to work effectively in a team environment and communicate technical concepts to both technical and non-technical stakeholders.
  
• Strong analytical, problem solving and diagnostic skills
  
• Ability to translate technical requirements into automation solutions (machine learning pipelines)
  
• Strong communication and collaboration skills for working effectively in a team environment.
  
• Self-driven and self managed according to changing priorities