Data Scientist & Experimentation Analyst

Data Scientist & Experimentation Analyst

This role sits at the intersection of data science, experimentation, and machine learning, supporting the development of advanced pricing and personalization solutions. You will be responsible for designing and analyzing experiments that directly influence ML model performance and business strategy. The position requires strong statistical thinking, hands-on analytical skills, and the ability to translate complex data into clear, actionable insights. You will collaborate closely with ML Scientists, Data Engineers, and Product teams to support end-to-end experimentation and model evaluation workflows. The environment is highly data-driven and fast-paced, with a strong focus on rigor, scalability, and measurable impact. This is a highly impactful role where your insights will shape decision-making and optimize machine learning outcomes across key business areas. You will also contribute to model support, feature engineering, and performance tracking in a collaborative, innovation-focused setting.

Accountabilities

• Design, execute, and analyze A/B tests and multivariate experiments to evaluate machine learning models and business strategies in pricing and personalization domains.
• Perform exploratory data analysis, hypothesis testing, and statistical modeling to generate insights that support ML development and business decision-making.
• Support machine learning scientists by preparing datasets, performing feature engineering, and assisting in model evaluation and performance tracking.
• Develop dashboards and data visualizations to monitor experiment outcomes, key performance metrics, and model behavior over time.
• Conduct deep-dive analyses on complex datasets to answer business questions and generate actionable recommendations for stakeholders.
• Collaborate with cross-functional teams including ML Scientists, Data Engineers, and Product Managers to align experimentation goals and ensure successful implementation.
• Ensure experimentation outputs are statistically sound, well-documented, and effectively communicated to both technical and non-technical audiences.

Requirements

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related quantitative field.
• 4+ years of experience in data science, experimentation analysis, or ML-supporting analytical roles.
• Strong knowledge of statistical methods, causal inference, and experimental design principles (A/B testing, multivariate testing).
• Proficiency in Python for data analysis and modeling using libraries such as Pandas, NumPy, and Scikit-learn.
• Strong SQL skills, including joins, window functions, aggregations, and complex querying.
• Experience with classical machine learning techniques such as regression, classification, clustering, and algorithms like XGBoost, Random Forest, and KMeans.
• Hands-on experience with data visualization tools such as Tableau, Power BI, Matplotlib, or Seaborn.
• Strong understanding of data preprocessing, feature engineering, and working with large, complex datasets.
• Excellent communication skills with the ability to present findings clearly and influence stakeholders.
• Strong problem-solving mindset with the ability to work independently and collaboratively in fast-paced environments.

Benefits

• Opportunity to work on high-impact machine learning and experimentation projects in pricing and personalization.
• Exposure to advanced ML workflows and collaboration with experienced data science and engineering teams.
• Fully remote or flexible work arrangements depending on team structure.
• Competitive compensation aligned with experience and market benchmarks.
• Collaborative, data-driven environment focused on innovation and continuous learning.
• Opportunity to work with modern data stacks and large-scale datasets.
• Strong emphasis on experimentation culture, professional growth, and skill development.