Staff Data Scientist I, Rewards Economy
Mistplay · Toronto, ON
- On site
- Full-time
- $150,000 / year
- Toronto, ON
Job highlights
- Lead rewards economy design and optimization.
- Develop machine learning driven reward systems.
- Analyze and optimize strategies for engagement.
- Drive AI strategy for user experience.
- Collaborate on loyalty and retention.
About the role
About Mistplay
Mistplay is the #1 loyalty app for mobile gamers. Our community of millions of engaged mobile gamers come to Mistplay to discover new games to play and earn rewards. Gamers are rewarded for their time and money spent within the games and can redeem those rewards for gift cards. Mistplay is on a mission to be the best way to play mobile games for everyone everywhere! Download Mistplay on the Google Play Store here and follow us on Instagram, Twitter and Facebook.Location
📍 Please Note: In Canada 🇨🇦, Mistplay follows a 2 days/week in-office hybrid model in Toronto (400 University Ave) & Montreal (1001 Blvd. Robert-Bourassa)Job Description
Mistplay is seeking an innovative Data Scientist to lead the design and optimization of our rewards economy. In this critical role, you will develop and balance reward systems to ensure they are enticing and add value for our users while maintaining cost-effectiveness for Mistplay. Collaborating with the Data and AI organization, you will lead initiatives in real-time predictive modeling and optimization within our machine learning-driven operations.What You'll Do at Mistplay
- You’ll be part of the cross functional group responsible for the overall rewarding and the loyalty economy strategy for Mistplay.
- Create and implement machine learning driven rewards economy systems and core loops.
- Continuously analyze and optimize the economy strategies and implementations to enhance engagement and drive business outcomes.
- Drive the strategy on how machine learning and AI can further advance the optimization for better user experience and business performance.
- Distill complex model outputs into actionable strategic insights for executive stakeholders, advocating for AI-first approaches to loyalty and retention.
- Design and productionize contextual bandits and RL frameworks to personalize reward distribution in real-time, ensuring the right user gets the right incentive at the right moment.
- Serve as the primary architect for Mistplay’s rewards economy, balancing complex variables to ensure a "win-win" scenario: maximum value for players and sustainable unit economics for the business.
- Partner with Product, Analytics, Engineering, and Finance to translate high-level business goals into algorithmic constraints and incentive structures.
What You'll Bring to Mistplay
- A combination of 8+ years in Data Science, Machine Learning, Quant, Econometrics, and/or Master’s or Ph.D. in Data Science, Machine Learning, or a related quantitative field.
- Understanding of reinforcement learning (DeepQ, contextual bandits)
- Expertise in implementing machine learning models using Python and PyTorch, paired with strong data analysis skills using SQL and Python.
- Fast and agile approach to exploring and implementing solutions with a strategic focus on the core loop and economy design.
- Experience and background in Quant, Data Science, or related fields, with a foundational understanding of economy and core loop designs in gaming or similar contexts.
- A passion for innovative AI solutions, with demonstrated knowledge in real-time predictive modeling.
- Prior experience in the mobile gaming industry with a focus on the economics of player behavior and engagement strategies is a strong nice-to-have.
Why Mistplay?
We strive to make our work environment as inviting and fun as possible! Working at Mistplay is coupled with a whole array of perks that we've adopted virtually and in-person: Team Lunches, game nights, company-wide events, and so much more. Our culture is deeply rooted in growth and upheld by a team of smart, dynamic, and enthusiastic people. We utilize data to constantly learn, improve, and adapt. We foster an environment where everyone is encouraged to share their ideas, push boundaries, take calculated risks, and witness their visions come to life.AI in Hiring
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.Key skills/competency
Staff Data Scientist Rewards Economy
- Data Science
- Machine Learning
- Reinforcement Learning
- Python
- PyTorch
- SQL
- Predictive Modeling
- Econometrics
- Quantitative Finance
- Mobile Gaming
Skills & topics
- Data Scientist
- Machine Learning Engineer
- Rewards Economy
- Loyalty Programs
- Python
- PyTorch
- SQL
- Reinforcement Learning
- Predictive Modeling
- Quantitative Finance
- Econometrics
- Mobile Gaming
- AI
- Data Analysis
- Optimization
- Real-time Modeling
- Gaming Economy
How to get hired
- Tailor your resume: Highlight your 8+ years of experience in Data Science, ML, Quant, or Econometrics, emphasizing Python, PyTorch, SQL, and reinforcement learning.
- Showcase relevant experience: Detail your understanding of core loops, economy design, and predictive modeling, especially if from the gaming industry.
- Prepare for technical interviews: Be ready to discuss your expertise in implementing ML models and analyzing data for business outcomes.
- Demonstrate strategic thinking: Articulate how you leverage AI and ML to enhance user experience and business performance in your past roles.
- Understand the hybrid model: Be prepared to discuss your ability to work in a hybrid model in Toronto or Montreal.
Technical preparation
Practice Python, PyTorch, and SQL extensively.,Review reinforcement learning algorithms and applications.,Build predictive models for real-time optimization.,Develop economy and core loop simulations.
Behavioral questions
Describe a complex economy you designed.,How do you balance user value and business goals?,Share an AI innovation you championed.,How do you collaborate with cross-functional teams?
Frequently asked questions
- What is the work arrangement for the Staff Data Scientist Rewards Economy role at Mistplay in Canada?
- For the Staff Data Scientist Rewards Economy position in Canada, Mistplay follows a hybrid model requiring 2 days per week in the office. This applies to both the Toronto (400 University Ave) and Montreal (1001 Blvd. Robert-Bourassa) locations.
- What technical skills are essential for the Staff Data Scientist Rewards Economy role at Mistplay?
- Essential technical skills include 8+ years in Data Science, Machine Learning, or Quant fields, with expertise in Python, PyTorch, SQL, and a strong understanding of reinforcement learning (DeepQ, contextual bandits) and real-time predictive modeling.
- Does Mistplay use AI in its hiring process for the Staff Data Scientist Rewards Economy position?
- Yes, Mistplay may use AI tools to assist with reviewing applications and analyzing resumes for the Staff Data Scientist Rewards Economy role. However, human judgment remains central to the final hiring decisions.
- What kind of experience is considered a strong advantage for the Staff Data Scientist Rewards Economy role at Mistplay?
- Prior experience in the mobile gaming industry, with a specific focus on the economics of player behavior and engagement strategies, is considered a strong nice-to-have for this role.
- What is Mistplay's approach to AI and data in the Staff Data Scientist Rewards Economy role?
- Mistplay fosters an environment where data is used to constantly learn, improve, and adapt. The Staff Data Scientist will drive the strategy on how machine learning and AI can advance optimization for better user experience and business performance.
- How does Mistplay ensure a 'win-win' scenario with its rewards economy?
- The Staff Data Scientist will serve as the primary architect for Mistplay’s rewards economy, balancing complex variables to ensure maximum value for players and sustainable unit economics for the business.
- What educational background is preferred for the Staff Data Scientist Rewards Economy position?
- A Master's or Ph.D. in Data Science, Machine Learning, or a related quantitative field is preferred, alongside a combination of 8+ years of experience in Data Science, Machine Learning, Quantitative Finance, or Econometrics.