Senior Data Scientist

The Senior Data Scientist leads the design, development, and implementation of advanced analytics, machine learning, and AI-driven solutions that generate measurable business value. This role independently manages complex data science initiatives from concept through deployment, transforming broad business challenges into analytical frameworks, developing and validating models, operationalizing solutions, and monitoring performance over time.

The successful candidate combines deep statistical expertise, strong engineering practices, and business acumen to deliver scalable, production-ready solutions. In addition to serving as a hands on technical contributor, the Senior Data Scientist provides leadership and mentorship to colleagues while partnering with stakeholders to communicate insights, recommendations, and strategic tradeoffs.

Senior Data Scientist Position Overview

The Senior Data Scientist leads the design, development, and implementation of advanced analytics, machine learning, and AI-driven solutions that generate measurable business value. This role independently manages complex data science initiatives from concept through deployment, transforming broad business challenges into analytical frameworks, developing and validating models, operationalizing solutions, and monitoring performance over time.

The successful candidate combines deep statistical expertise, strong engineering practices, and business acumen to deliver scalable, production-ready solutions. In addition to serving as a hands on technical contributor, the Senior Data Scientist provides leadership and mentorship to colleagues while partnering with stakeholders to communicate insights, recommendations, and strategic tradeoffs.

• Design, develop, and implement advanced statistical, predictive, machine learning, and artificial intelligence models that support critical business objectives.
• Establish model evaluation frameworks, performance metrics, and monitoring strategies to ensure ongoing reliability and business impact.
• Identify and resolve common modeling challenges, including data quality limitations, bias, model drift, transformations, prompt optimization, retrieval methodologies, hallucination mitigation, and model assumption validation.
• Research and evaluate analytical approaches, model architectures, and statistical techniques to address complex business problems.
• Collaborate with business leaders to identify opportunities where analytics and AI can improve processes, drive efficiency, and unlock value from available data assets.
• Translate complex analyses into clear, actionable insights and present recommendations to both technical and non-technical audiences.
• Connect operational, financial, customer, and performance metrics to analytical solutions that drive measurable outcomes.
• Serve as a trusted advisor and subject matter expert within assigned functional areas.
• Mentor and support the development of data scientists and cross-functional technical teams.
• Create reusable and scalable AI and analytics components that promote flexibility, consistency, and long-term maintainability.
• Partner with engineering, product, and data teams to deploy solutions into production environments and support ongoing enhancements.
• Develop and maintain documentation related to solution design, methodologies, assumptions, data definitions, and known limitations.
• Perform additional duties and responsibilities as required.

• Advanced expertise in Python for analytics, machine learning, artificial intelligence, and solution development.
• Strong proficiency in SQL and relational database concepts.
• Deep understanding of Generative AI, large language models (LLMs), and practical enterprise applications.
• Experience applying software engineering best practices, including Git, peer code reviews, testing, and modular development.
• Demonstrated success developing, deploying, and monitoring models in cloud-based environments.
• Working knowledge of MLOps practices, model lifecycle management, and experiment tracking tools.
• Familiarity with AI-assisted development tools such as Git Hub Copilot, Cursor, Claude Code, or similar technologies.
• Experience working with cloud-based data platforms and infrastructure, including Snowflake, AWS, and related technologies.
• Expertise in one or more of the following areas:
  • Commercial analytics, including pricing strategy, customer segmentation, retention, churn analysis, and customer lifetime value.
  • Operational analytics, including logistics, route optimization, resource planning, and predictive maintenance.
  • Customer experience analytics, including sentiment analysis, Net Promoter Score (NPS), containment rates, deflection rates, and average handle time.
• Master's degree in Statistics, Mathematics, Computer Science, Engineering, Information Management, or a related quantitative discipline preferred.

• Bachelor's degree in Statistics, Mathematics, Computer Science, Engineering, Information Management, or a…