Senior Data Scientist (Reinforcement Learning)

• Building Reinforcement Learning models to solve business problems in a production environment.
• 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.

• Extensive experience shipping Reinforcement Learning models
• 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.

Plusgrade is an equal-opportunity employer and is committed to providing an accessible recruitment process. We welcome applications from all qualified individuals and are committed to equal employment opportunities regardless of gender identity or expression, race, ethnic origin, creed, place of origin, age, sex, marital status, physical or mental disability, sexual orientation, and any other category protected by law. Upon request we will provide accommodation for applicants with disabilities. If you have a preference regarding the language of your interviews, please inform our Talent Team when they reach out to you.

We believe in diversity and inclusivity and that is why our interview process is designed for a positive candidate experience and to ensure every candidate is evaluated equally. We may utilize technology-assisted/ artificial intelligence tools to help us screen, assess, and select applicants for this position. This job posting is for an existing vacancy.

All applications will be reviewed from our Talent Team and the successful candidate(s) will go through the following recruitment process:

• Recruiter Phone Interview

• Hiring Manager Interview

• Test or case study or take-home assessment (if applicable)

• Team Interview

All candidates will be provided with feedback regardless if they pass or didn’t pass any of our interview stages. All your information will be kept confidential.