About the role

Senior Data Scientist at Plusgrade leveraging machine learning for dynamic pricing and offer allocation. Collaborating with cross-functional teams to enhance data-driven decision-making across diverse environments.

Responsibilities

Drive significant incremental business value by leveraging advanced machine learning to design, test, and implement advanced data science approaches for dynamic pricing, real-time offer allocation, and personalization, improving targeting and offer assignment within our marketing engine.
Develop and optimize algorithms that balance business constraints, customer behavior, and engagement objectives to deliver optimal, data-driven decisions across offers and pricing.
Design, enhance, and generalize models into scalable solutions that can be applied across products, partners, and diverse data environments.
Leverage a wide range of data sources (e.g., partner, product, and third-party data) to enrich algorithms and clearly demonstrate measurable business impact.
Lead and collaborate with cross-functional teams (Product, Engineering, Analytics) to establish best practices for developing, automating, and standardizing advanced data science solutions, with an emphasis on real-time applications.
Champion scalable, automated production deployments by integrating algorithms into live systems through rapid iteration and experimentation, leveraging AWS infrastructure (particularly SageMaker) to deploy, monitor, and scale models in production.

4+ years of experience researching, designing, and developing machine learning algorithms, with a strong focus on solving real-world business problems.
Expertise in developing algorithms for real-time decision-making or dynamic optimization problems, such as offer allocation, continuous pricing, or recommender systems.
Proficiency in machine learning, large-scale data processing, predictive analytics, and optimization techniques.
Strong programming skills in Python, with hands-on experience using machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Advanced SQL skills and familiarity with relational databases, enabling efficient manipulation of large and complex datasets.
Hands-on experience working in AWS environments, particularly with SageMaker for building, training, deploying, and monitoring machine learning models at scale.
The ability to conceptualize, design, and communicate complex algorithms to technical and non-technical stakeholders clearly and concisely.
Innate curiosity to solve complex problems, derive actionable insights and iterate on innovative solutions.
Strong business acumen and an ability to align data science initiatives with commercial goals, ensuring measurable business impact.
A quantitative Master's or Ph.D. is required, or equivalent experience. Relevant fields include, but are not limited to, Computer Science, Engineering, Mathematics, Statistics, and Operations Research.