Lead Data Scientist - Remote

Overview

RyzLabs is seeking a highly skilled and experienced Lead Data Scientist to join our growing analytics team. The ideal candidate will possess a deep understanding of statistical modeling, machine learning, and data engineering, with a proven track record of applying these skills to solve complex business problems, particularly within the e-commerce domain.

In Short

• Lead the design, development, and deployment of advanced predictive models and machine learning algorithms.
• Collaborate with product, engineering, and business teams to identify data science opportunities.
• Perform in-depth exploratory data analysis on large datasets.
• Develop and maintain robust data pipelines.
• Communicate complex analytical findings to various audiences.
• Mentor and guide junior data scientists.
• Stay updated on advancements in data science and e-commerce trends.
• Focus on optimizing e-commerce operations through data-driven solutions.
• Evaluate the applicability of new data science techniques to business needs.
• Foster a culture of continuous learning and innovation within the team.

Requirements

• Master's or Ph.D. in a quantitative field such as Statistics or Computer Science.
• 6+ years of experience in data science, with 3+ years in e-commerce.
• Proven leadership experience in data science projects.
• Expert-level proficiency in Python and SQL.
• Extensive experience with machine learning techniques.
• Exceptional problem-solving and analytical skills.
• Ability to translate business problems into actionable solutions.
• Experience with Marketing Mix Modeling or Multi-Touch Attribution is a plus.
• Familiarity with cloud platforms and big data technologies is desirable.
• Knowledge of causal inference methods is a bonus.

Benefits

• Opportunity to work in a remote and distributed team.
• Autonomy and ownership of development projects.
• Environment of opportunities, learning, and growth.
• Challenging projects with a team of professionals.
• Focus on customer-centric decision-making.
• Encouragement of a bias for action and urgency.
• Commitment to continuous improvement and raising standards.
• Respectful and humble workplace culture.
• Frugality and cost-consciousness in operations.
• Impact-driven approach to getting things done efficiently.