As a Machine Learning Scientist, you will design, build, and deploy advanced models that guide pricing and promotional optimization across our company. You will work closely with other scientists, engineers, analysts, and product teams to translate complex business challenges into scalable, data-driven solutions that deliver measurable impact.
Key Job Responsibilities and Duties:
Develop and deploy models for causal inference, uplift estimation, and optimization to measure and maximize the incremental effect of price and promotion decisions.
Design and improve dynamic pricing algorithms that balance competitiveness, conversion, and profitability.
Contribute to the development of platform capabilities, enhancing experimentation, simulation, and decision-support capabilities.
Partner with product and business stakeholders to translate scientific insights into actionable strategies.
Stay up to date with the latest advances in machine learning, causal modeling, and pricing optimization, and apply them pragmatically at scale.
Qualifications & Skills:
MSc or PhD (or equivalent experience) in a quantitative field such as Computer Science, Statistics, Economics, Operations Research, Mathematics, Engineering, Artificial Intelligence, or Physics.
Relevant professional or academic experience applying Machine Learning to business problems (typically MSc + 5 years, or PhD + 3 years).
Proven track record designing and executing end-to-end research and development projects, and generating measurable impact through large-scale ML model development. Evidence such as peer-reviewed publications, patents, or open-source contributions is a plus.
Advanced knowledge and experience in Causal Inference, Uplift Modeling, Reinforcement Learning, Active Learning, and/or Optimization.
Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, XGBoost).
Experience working with large-scale data systems and production ML pipelines.
Solid understanding of data analytics, A/B testing, and statistical experimentation.
Experience with distributed computing and data technologies such as Spark, Hadoop, Kafka, and SQL.
Familiarity with version control systems and software engineering best practices.
Experience collaborating cross-functionally with developers, analysts, product managers, and UX specialists to deliver machine learning-driven products.
Ability to communicate complex scientific and technical ideas clearly and effectively to both technical and non-technical audiences.
Excellent English communication skills, both written and verbal.
