Staff Data Scientist I (AI/Machine Learning)

TL;DR

Staff Data Scientist I (AI/Machine Learning): Leading the design and optimization of reward systems and loyalty economy for a mobile gaming app with an accent on real-time predictive modeling, machine learning-driven operations, and economy design. Focus on creating and implementing ML-driven reward systems, continuously optimizing strategies for engagement, and advancing AI for user experience and business performance.

Location: Hybrid in Toronto or Montreal, Canada (2 days/week in office required)

Company

hirify.global is the #1 loyalty app for mobile gamers, enabling users to discover new games and earn rewards.

What you will do

• Lead the design and optimization of hirify.global's rewards economy.
• Create and implement machine learning-driven reward economy systems and core loops.
• Continuously analyze and optimize economy strategies to enhance engagement and drive business outcomes.
• Drive the strategy for how machine learning and AI can advance optimization for better user experience and business performance.
• Be part of the cross-functional group responsible for the overall rewarding and loyalty economy strategy.

Requirements

• 8+ years in Data Science, Machine Learning, Quant, Econometrics, and/or a Master’s or Ph.D. in a related quantitative field.
• Understanding of reinforcement learning (DeepQ, contextual bandits).
• Expertise in implementing machine learning models using Python and PyTorch.
• Strong data analysis skills using SQL and Python.
• A foundational understanding of economy and core loop designs in gaming or similar contexts.
• Demonstrated knowledge in real-time predictive modeling.

Nice to have

• Prior experience in the mobile gaming industry with a focus on player behavior economics and engagement strategies.

Culture & Benefits

• Strive to make the work environment inviting and fun, offering team lunches, game nights, and company-wide events.
• Culture rooted in growth, supported by smart, dynamic, and enthusiastic people.
• Utilize data to constantly learn, improve, and adapt.
• Foster an environment where everyone is encouraged to share ideas, push boundaries, and take calculated risks.