The Data Scientist role as such is responsible for:

Model Development & Optimization

• Design, build, and optimize machine learning models, statistical techniques, and business logic to solve high-impact problems.
• Collaborate closely with Data Analysts to conduct rigorous testing, monitor model health, and implement enhancements based on performance insights.
• Work in tandem with Data Engineers to facilitate seamless implementation and deployment, coordinating timelines and technical requirements.

Technical Collaboration & Backlog Prioritization

• Align with Product Owners (POs) on backlog management, model prioritization, and task sequencing to maintain focus on high-impact projects.
• Effectively translate complex technical tasks into organized development routines and timelines, ensuring that the PO and other stakeholders have a clear understanding of model progress and expectations.
• Act as a technical partner in discussions with Data Engineers, contributing to conversations on data architecture and engineering needs.

Documentation & Stakeholder Communication

• Document all model developments, feature updates, and optimizations, providing comprehensive technical details.
• Support the PO in translating technical documentation for commercial teams, ensuring alignment and clarity on algorithm functionality and commercial implications.

Business Insight & Strategy Alignment

• Apply strong business acumen to ensure models address relevant business questions and drive impactful insights.
• Maintain awareness of business challenges, continuously refining models to meet changing business demands and enhance strategic value.
• Stay current with industry trends and emerging methodologies, continuously integrating best practices to enhance model relevance and effectiveness.

Minimum Requirements:

• Degree in Mathematics, Statistics, Data Science, or a related field with a strong foundation in calculus, linear algebra, probability, and statistics.
• Proficiency in SQL, Python, and PySpark, with extensive programming knowledge.
• Expertise in machine learning techniques, statistical modeling, classical optimization, and search algorithms.
• Strong knowledge of data engineering concepts and data architecture, important for facilitating technical conversations with engineering counterparts.
• Very strong problem-solving skills with an ability to manage complexity and ambiguity.
• Proven track record managing all aspects of a successful product throughout its lifecycle.
• Knowledge and experience with the application of CRISP-DM and Agile methodologies.
• Ability to translate complex technical tasks into organized routines and timelines; Must be able to translate backlog and timelines through Product Manager to ensure commercial teams have clear understanding of expectations for algo development & implementation.
• An extensive data science background is critical, and experience in sales/operations roles is a plus, but not required.