Job Summary: Square Enix is a publisher of entertainment contents, primarily known for digital games such as Final Fantasy series, Kingdom Hearts, Dragon Quest, NieR, Life is Strange and Just Cause. Our mission is to create and deliver entertainment contents which resonates with hearts and minds of customers.

Scroll down to find an indepth overview of this job, and what is expected of candidates Make an application by clicking on the Apply button.The Senior Data Scientist, Recommendation will be a passionate leader focused on providing optimized and personalized user experiences powered by the application of machine learning. A key member of the Data Science & AI Team, the Sr. Data Scientist will build, manage, and improve our communications with fans via large-scale models that drive engagement.The successful candidate will excel at solving problems, delivering effective recommender system projects, and ensuring the process is streamlined, efficient, and continuously optimized. Adept at managing system implementations across various stages of development, the Senior Data Scientist, Recommendations should be as comfortable with early-stage Proof of Concept advocacy, project management, and internal workflow creation, as with introducing ML Ops practices and enhancing existing projects with more sophisticated methods. This role will lead mid or junior level data scientist(s) and collaborate with Data Engineering, DevOps, and business stakeholders to ensure the accurate implementation and impact of recommender systems deployed.Key Deliverables:

Build and deploy scalable data science models/algorithms to drive marketing, promotion, and personalization actions that provide measurable improvement.Identify, analyse, and interpret users’ in-game/outer-game behavioural data, and apply analytics and machine learning methods.Design, construct and maintain predictive models including, but not limited to, social behaviour, retention and monetization to increase the lifetime value of our customers.Provide ongoing maintenance and support for deployed machine learning models, ensuring their reliability and effectiveness in real-world applications.Continuously improve our solutions to make them more simple, robust, efficient and scalable. This includes pipeline design and continuous improvement schemes through machine learning.Able to effectively manage existing code base, document past experiences, automate processes, and create feedback loops.Lead junior/mid data scientists inside/outside of the team who works on recommendation projects.Promote best practices in machine learning system deployment, testing, and evaluation.Project management: Initiate PoC, create workflow with partner teams, deliver results for project approval, set schedules and priorities, and document results.Remain alert to opportunities which further utilize our data or data science methods to benefit the business, operations, or key strategic initiatives.Knowledge & Experience:

Essential:Extensive experience of proven experience as a Data Scientist and/or Product Engineer, working on similar projects such as user communication, service optimization and personalization.Practical experience in methodologies used in recommender system such as Collaborative Filtering, Content Based Recommendation, Matrix Factorization.Experience with the management of ML code base and experimentation result in an organized and efficient manner.Experience with cloud platforms and technologies for deploying and managing machine learning models at scale, such as AWS, Azure, or Google Cloud Platform.Entrepreneurial and curious mindset with a passion for experimentation and innovation combined with practical business instincts; can both dream big as well as execute and prioritize projects aligned to strategic needs.Experience or desire to manage, mentor, and train Jr Data Scientists.Competencies, Skills & Attributes:

Essential:Proficiency in data analysis, data mining and programming languages preferably with SQL, Python, TensorFlow, PyTorch, or scikit-learn.Practical experience in ML ops, such as Python packaging, Docker/Kubernetes, CI/CD, deployment and monitoring of ML models’ performance.Our goal at Square Enix is to hire, retain, develop and promote the best talent, regardless of age, gender, race, religious, belief, sexual orientation or physical ability.Hybrid Working Policy Square Enix is pleased to be an employer that offers flexibility within the workplace. We have a hybrid working policy which allows employees to work from the comfort of their home, three days per week, and in our amazing Blackfriars office for the other two. Or, if being in the Office is your preference, you can choose three days working from our office and two days working from home. The choice is yours!

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