Hi
Greetings from Conch Technologies
Position: Data Scientist ( Revenue Management exp )
Location: Remote
Duration: 6+ month contract
Minimum Experience and Qualifications
- Bachelor's Degree in computer science or a quantitative discipline; OR demonstrated capability to perform job responsibilities with a High School Diploma/GED and at least four (4) years of previous relevant work experience
- Two (2) years of relevant industry experience
- Proficiency in Python, Structured Query Language (SQL, git, and common data science and machine learning libraries
- Experience building machine learning models
- Able to write production-quality code and be familiar with software engineering best practices, including testing and version control
- Ability to communicate their findings to both technical and non-technical audiences
- Ability to successfully complete technical interviews and take-home tests in areas of statistics, machine learning, SQL, Python, PySpark, optimization, and MLOps.
- Proficiency in statistical analysis and hypothesis testing
- Full proficiency in Excel
- Proficiency in Tableau or similar business intelligence tools
- Available for overnight travel (10%)
- Must pass a pre-employment drug test
- Must be legally eligible to work in the country in which the position is located
- Preferred Experience and Qualifications
- Advanced degree in computer science or a quantitative discipline
- Three (3) years of Data Science and/or Operations Research experience in either an airline or Revenue Management setting (airline RM preferred)
- Experience with revenue management, dynamic availability, and/or continuous pricing systems
- Experience applying various Machine Learning techniques such as: Time Series Forecasting, Recommendation Systems, Dynamic Pricing, Logistics Optimization, Churn Analysis, Segmentation Analysis, Natural Language Processing, Deep Learning, and Reinforcement Machine Learning
- Experience designing and conducting A/B testing and leveraging natural experiments where applicable
- Experience using Databricks, PySpark / Spark, Azure Cloud, Kubernetes, Docker, PyTorch, Keras, TensorFlow