Lead Quantitative Strategist

At Findojobs-Za, we're building a platform that's revolutionizing the travel industry.

We're seeking an exceptional Quantitative Analysis Director to lead our team in developing advanced statistical and machine learning models.

In this role, you'll leverage your expertise to build the quantitative backbone of our business, optimizing pricing, predicting market shifts, and delivering strategic insights that drive the future of travel.

1. Develop and execute the 'trading floor' quantitative strategy for Findojobs-Za, integrating real-time market data to optimize hotel and travel inventory.
2. Design, implement, and maintain complex statistical and machine learning models to analyze booking patterns, customer behavior, and travel market trends.
3. Create predictive models to forecast demand, optimize pricing, and improve inventory allocation—mirroring real-time risk management approaches seen on trading floors.
4. Conduct high-impact A/B testing and other experimental designs to evaluate product and pricing changes.
5. Develop and refine dynamic pricing algorithms, demand forecasting models, and recommendation engines that can handle large-scale data in real-time.
6. Implement optimization solutions for route management, cost minimization, and personalized travel experiences.
7. Translate complex data analyses into actionable insights for C-level stakeholders, product teams, and other decision-makers.
8. Build and maintain dashboards and data visualizations, providing oversight of key business metrics—similar to a trading floor's real-time analytics.
9. Collaborate closely with product, engineering, and business teams to deploy data-driven solutions in production.
10. Ensure data accuracy, consistency, and integrity through rigorous validation practices.

1. Bachelor's or Master's degree in Mathematics, Statistics, Computer Science, Data Science, or a related field. PhD a plus.
2. 5+ years of experience in a trading floor (or equivalent) environment, building and deploying advanced quantitative models.
3. Proven track record in quantitative analysis, data science, or a similar role, preferably in the travel or tech industry (though trading experience is the priority).
4. Strong proficiency in programming languages such as Python or R, especially for statistical computing and machine learning.
5. Experience with data querying languages (SQL) and data visualization tools (e.g., Tableau, Power BI).
6. Extensive knowledge of machine learning techniques (supervised/unsupervised learning, time-series forecasting) and a deep understanding of how to apply them in real-time trading or dynamic pricing contexts.
7. Familiarity with big data frameworks (Spark, Hadoop) is a plus.

1. Exceptional analytical and problem-solving abilities, honed in fast-paced environments where real-time decisions are critical.
2. Excellent communication skills, with the ability to articulate complex quantitative concepts to both technical and non-technical stakeholders.
3. Detail-oriented, with a passion for finding innovative data-driven solutions and a relentless drive for continuous improvement.