At arenaflex, we're on a mission to create a world where anyone can belong anywhere. Our Customer Support (CS) team is dedicated to building the world's most loyal travel community through exceptional service. Personalizing our CS offerings will allow us to meet our customers when and how they need us most. As a Data Scientist working on Inference in CS, you will have the opportunity to collaborate with a strong team of engineers, product managers, designers, and operation agents to build scalable and robust systems to match our services to specific customer needs. You will be able to create meaningful impact through deep scientific understanding and by designing interventions for arenaflex CS's most critical challenges.
**The Difference You Will Make:**
For this role, we're seeking a Causal Inference/Operations Research expert to join the Customer Support Data Science team. You will work closely with the tech lead for this area on significant components of larger projects and have a direct opportunity to contribute and influence in the CS Personalization space by designing scalable scientific solutions for problems like:
* Creating causal models to identify segments of users with unique CS needs and develop algorithms to match users with the appropriate CS path.
* Determining allocation strategy of Make Goods budget to maximize business impact.
* Estimating the long-term impact of changes in the CS experience for targeted user groups.
* Providing strategic insights on tradeoffs between CS quality and cost to empower us to offer the best possible CS experience to our customers.
* Optimizing CS routing engines with advanced algorithms and developing simulations to validate them.
* Building a deep understanding of our individual agent's abilities to improve CS quality.
* Measuring the CS personalization products through rigorous experimentations and driving data-informed launch decisions.
**A Typical Day:**
* Build segmentation models for various CS scenarios including data exploration, optimization, and model prototyping.
* Work collaboratively with cross-functional partners including software engineers, product managers, operations, and research, to refine requirements for segmentation models, drive scientific decisions, and quantify impact.
* Develop strategies to match users with the appropriate CS solutions.
* Design and implement quasi-experiment frameworks to measure feature launch effectiveness when A/B testing is not feasible.
* Integrate causal modeling with ML models in order to improve performance of model-derived interventions.
* Regularly present work internally at monthly meetings to technical, engineering, and product stakeholders to iterate and generate excitement on roadmap progress.
* Bring new ideas to the team which can improve the operational efficiency of customer support at arenaflex.
**Your Expertise:**
* 2+ years of relevant industry experience (e.g., ML scientist, tech lead, junior faculty) and a Master's degree or PhD in relevant fields.
* Strong fluency in Python or R for hands-on IC work and SQL for advanced data analysis at scale.
* Experience with causal inference and experimentation techniques, ideally in a multi-sided platform setting.
* Proven ability to communicate clearly and effectively to audiences of varying technical levels.
* Proven mix of strong intellectual curiosity with high-level pragmatism and engagement with the technical community. Publications or presentations in recognized journals/conferences are a plus.
* Ability to take a product-oriented mindset in using conceptual and innovative thinking to develop and apply solutions taking into consideration the user experience.
**Your Location:**
This position is US-Remote Eligible. The role may include occasional work at an arenaflex office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where arenaflex, Inc. has a registered entity. If your position is employed by another arenaflex entity, your recruiter will inform you what states you are eligible to work from.
**Our Commitment To Inclusion & Belonging:**
arenaflex is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services, and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability-inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: [insert email]. Please include your full name, the role you're applying for, and the accommodation necessary to assist you with the recruiting process.
**Why Join arenaflex?**
* Collaborate with a talented team of engineers, product managers, designers, and operation agents to build scalable and robust systems.
* Create meaningful impact through deep scientific understanding and by designing interventions for arenaflex CS's most critical challenges.
* Work on a wide range of projects, from building segmentation models to optimizing CS routing engines.
* Develop your skills and expertise in causal inference, experimentation, and machine learning.
* Enjoy a dynamic and inclusive work environment that values diversity and creativity.
* Participate in regular feedback and growth opportunities to help you achieve your career goals.
**What We Offer:**
* Competitive salary and benefits package.
* Opportunity to work on a wide range of projects and contribute to the growth and success of arenaflex.
* Collaborative and dynamic work environment.
* Professional development and growth opportunities.
* Flexible work arrangements, including remote work options.
* Access to cutting-edge technology and tools.
* Recognition and rewards for outstanding performance.
**How to Apply:**
If you're a motivated and talented Data Scientist with a passion for Operations Research and Customer Support, we encourage you to apply for this exciting opportunity. Please submit your resume, cover letter, and any relevant work samples or publications. We can't wait to hear from you!