Staff Machine Learning Engineer - Pricing

Do you want to join a Machine Learning team committed to enhancing the PlayStation experience for hundreds of millions of users and also game studios? The work we do delivers highly impactful pricing recommendations to our studios and has strong leadership sponsorship. The Machine Learning Engineers within the DTIS group will deliver optimized interactions across PlayStation experiences by designing, coding, training, documenting, cost-effectively deploying, and evaluating business-critical machine learning and AI systems.

We are looking for someone who can architect and build delightful production-grade products for customer-facing experiences, in an agile environment, collaborating with teams across Engineering, Product and Business. You will work on a project backed by executive leadership and poised to significantly influence revenue and user engagement.

• Lead the design and implementation of ML models that quantify product price-elasticity and cannibalization effect to support multi-period promotions.
• Own time-series forecasting algorithms to predict demand across multiple regions and product lines.
• Develop optimization workflows (OR-Tools) that balance sales growth and business constraints.

• Refine clustering algorithms to identify lookalike products for smarter pricing and promotional strategies.
• Architect and lead the implementation of the full machine learning development cycle, including feature engineering, automated model training, automated evaluation, and deployment, and conduct rigorous testing and validation to ensure accuracy, efficiency, and scalability.
• Apply a broad spectrum of state-of-the-art machine learning and deep learning technologies in the areas of classification, regression, clustering, time series forecasting, uplift modeling, and synthetic data generation.
• Work closely with cross-functional partners in business and product to translate requirements into effective, scalable, ML-driven solutions.
• Collaborate with other engineering functions in Client, Server, and QA to drive ML integrations and deliver results.
• Collaborate with ML application teams that power product personalizations and search to further enhance the core experiences of our players. 
• Mentor and guide junior machine learning engineers, providing technical leadership and raising the bar of technical quality.
• Thought-lead and influence technology strategy and product roadmap through ML/AI lens, contributing into the development and execution of overall ML/AI strategy. 

• Advanced degree (Master’s or Ph.D.) in CS/Statistics/Data Science/Operations Research/Econometrics, specializing in machine learning and optimization problems.
• 3+ years of industry experience applying time-series models in production, with hands-on experience in forecasting libraries (e.g. LightGBM, Prophet).
• Proven track record with mixed-integer programming or constraint solvers for pricing or resource allocation.
• Strong programming skills in Python, Java, with proficiency in ML libraries and frameworks like TensorFlow, PyTorch, Spark Mlib, and scikit-learn, and with ML systems engineering experience (e.g. orchestration, pipelines, serving, monitoring).
• Excellent communication skills and ability to bridge technical solutions with business objectives, to convey complex concepts to stakeholders with little knowledge of ML.
• Expertise in measuring the performance unsupervised and supervised models.
• Proven track record of successfully delivering large-scale commercial machine learning products from conception to production, in technical leadership roles. 
• Experience with software engineering principles, modern ML tech stacks, and use of Databricks, Snowflake, Tecton, and cloud services like AWS for machine learning development and deployment.
• Excellent problem-solving skills, with the ability to navigate through ambiguity to drive results.

• Previous work with revenue-management systems.
• Experience in a variety of machine learning and AI techniques, such as forecasting models, classification models on structured data, embeddings, clustering, supervised and unsupervised learning models, deep learning, and optimization.
• Experience working in fast-paced environments, on business-critical missions.
• Experience in e-commerce, digital goods, or gaming industries.
• Experience with recommender systems and similarity matching algorithms.
• Background in experimentation design, causal inference, uplift modeling, online A/B testing frameworks, and relevant metrics.
• Experience with large-scale structured data manipulation and processing. 
• Experience in building large-scale online customer-facing microservices.
• Experience working with custom ML platforms for continuous integration, deployment, and monitoring of machine learning models.
• Contributions to the machine learning community through publications, open-source projects, or presenting at conferences and workshops.