Full-Time Data Scientist

Oversee is revolutionizing business travel management with cutting-edge technology for price assurance, advanced analytics, and reporting. We help clients optimize programs, cut costs, and enhance traveler experiences.

We're seeking a Data Scientist to design and implement statistical and machine learning models for dynamic pricing, personalized recommendations, and anomaly detection. This role involves:

1. Designing and implementing statistical and ML models to solve key business problems (e.g., dynamic pricing, personalized recommendations, anomaly detection).
2. Conducting advanced statistical analyses and controlled experiments to quantify product impact.
3. Collaborating with engineering and product teams to integrate data solutions into production.

Your impact:

• Enable clients to discover hidden savings and insights from travel data.
• Build data-driven products managing billions in Air and Hotel Spend.
• Drive data innovation for Oversee's next-generation intelligent travel tools.

Requirements:

• 5+ years of applied industry experience as a Data Scientist
• Degree in Computer Science, Statistics, Mathematics, or a related field
• Expert Python skills and fluency with the data science ecosystem (Pandas, NumPy, scikit-learn, PySpark)
• Strong understanding of statistical analysis, modeling, and machine learning algorithms
• Strong SQL skills and experience with large-scale data warehouses (e.g., Snowflake, Redshift, BigQuery)
• Proven track record of shipping and monitoring ML models in production
• Strong communication skills to translate complex findings into clear insights
• Collaborative mindset with a passion for continuous learning and improvement

Advantages:

• Domain experience in travel or transportation
• Familiarity with GDS systems (Sabre, Amadeus, Travelport)
• Hands-on experience with AWS and modern data engineering tooling (Snowflake, dbt)
• Contributions to open-source projects or hackathon wins
• Familiarity with agile development methodologies and cross-functional squads

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