Lead Data Scientist

Summary

Join RyzLabs' growing analytics team as a Lead Data Scientist, leveraging your expertise in statistical modeling, machine learning, and data engineering to solve complex e-commerce problems. Lead the development and deployment of predictive models, collaborate with cross-functional teams, perform in-depth data analysis, and mentor junior team members. Communicate findings effectively to both technical and non-technical audiences. Stay updated on the latest advancements in data science and e-commerce trends. This role requires a proven track record in e-commerce and strong leadership skills.

Requirements

• Master's or Ph.D. in a quantitative eld such as Statistics, Mathematics, Computer Science, Economics, Operations Research, Engineering, or a related discipline
• Minimum of 6+ years of progressive experience in data science, with at least 3 years specically focused on e-commerce, retail, or a similar high-volume consumer-facing industry
• Demonstrated experience in building and deploying data science models across a large and diverse assortment of products
• Proven leadership experience, including leading projects and mentoring junior team members
• Expert-level prociency in Python, including libraries essential for data science (e.g., scikit-learn, TensorFlow, PyTorch, Keras, Pandas, NumPy, SciPy)
• Strong command of SQL for data extraction and manipulation from various databases
• Extensive experience with machine learning techniques (e.g., supervised, unsupervised, reinforcement learning), statistical modeling, hypothesis testing, and experimental design (A/B testing)
• Exceptional problem-solving abilities, with a strong analytical mindset and aention to detail
• Ability to translate business problems into quantitative questions and actionable solutions

Responsibilities

• Lead the design, development, and deployment of advanced predictive models, machine learning algorithms, and data-driven solutions to optimize various aspects of our e-commerce operations (e.g., personalization, recommendation engines, pricing, inventory management, fraud detection, customer lifetime value)
• Collaborate with product, engineering, and business teams to identify opportunities for data science applications and dene project scopes
• Perform in-depth exploratory data analysis to uncover trends, paerns, and insights from large, complex datasets across a wide assortment of products
• Develop and maintain robust data pipelines and ensure data quality and integrity for modeling initiatives
• Communicate complex analytical ndings and recommendations clearly and concisely to both technical and non-technical audiences
• Mentor and guide junior data scientists, fostering a culture of continuous learning and innovation within the team
• Stay abreast of the latest advancements in data science, machine learning, and e-commerce trends, and evaluate their applicability to our business

Preferred Qualifications

• Experience with Marketing Mix Modeling (MMM) or Multi-Touch Aribution (MTA) models for marketing aribution
• Experience with big data technologies (e.g., Spark, Hadoop, Flink)
• Familiarity with cloud plaorms (e.g., AWS, GCP, Azure) and their data science services
• Experience with notebook visualization tools (Hex, Deepnote, Jupyter Notebooks, etc.)
• Knowledge of causal inference methods