Playtika is looking for a
Lausanne, Switzerland.
Over the past 5 years, we have implemented a real-time recommendation engine based on Bayesian multi-armed bandits (see our ). Our solution is now expanding to more use-cases and needs more algorithmic capabilities (quantitative recommendations, sequential recommendations, and bundle recommendations).
Responsibilities:- Discuss business opportunities with stakeholders, and propose relevant DS product/solutions
- Propose, lead, and conduct ML research applied to users’ recommendations and experience personalization
- Identify, propose and perform the development of new DS prototypes
- Analyze & demonstrate the impact of our solutions in real-time production
- Own the value of DS solution: from scoping & prototyping throughout entire production life. Identify the need to adjust and pivot, be able to negotiate requirements/capabilities with the stakeholders.
- Enable transfer and best usage of the solution by stakeholders after value demonstration
- Propose & contribute to scientific papers, blogs, conferences, etc.
- Mentor DS/MLE colleagues and act as DS Subject Matter Expert within the organization
Requirements:- M.Sc. in Data Science / Statistics / Engineering or others quantitative field
- 5+ years of work experience
- Strong problem-solving and scientific reasoning skills, to efficiently shape and drive complex data science projects
- Deep theoretical and practical knowledge of Machine Learning and/or Bayesian statistics
- Proficiency in Python, working experience with is an advantage
- Experience with causal inference is a significant advantage
- Ability to present complex projects to non-experts (e.g., product managers, business domain experts, executives)
Nice to have
- PhD in Data Science / Statistics / Engineering or others quantitative fields
- Experience with recommender systems (e.g., multi-armed bandits)
- Proficiency in SQL, working experience of GIT
- Knowledge of Hive and Hadoop, plus notions of Spark
- Experience with model deployment, CI/CD not required, but a plus