Data Scientist & Experimentation Analyst

Job Summary: The client is seeking a Data Scientist & Experimentation Analyst to play a critical role in supporting the development and evaluation of ML-driven pricing and personalization solutions. This role provides data-driven insights and rigorous experimentation, enabling end-to-end support for machine learning initiatives. The ideal candidate will excel in statistical analysis, experiment design, and data science workflows while supporting ML Scientists in building robust models and analyzing their performance.

Qualifications

• 4 years in data science, experimentation analysis, or a related role supporting ML projects and experimentation.
• Strong understanding of statistical methods, experiment design, and causal inference techniques.
• Proficiency in Python for data manipulation & machine learning (Pandas, NumPy, sci-kit-learn).
• Intermediate skills in SQL for data querying, including Window Functions, Joins, and Group By
• Familiarity with classical ML techniques like Classification, Regression, and Clustering, using algorithms like XGBoost, Random Forest, and KMeans.
• Experience with data visualization platforms (e.g., Tableau, Power BI, Matplotlib, or Seaborn).
• Proficiency in designing and analyzing A/B and multivariate experiments, focusing on drawing actionable insights.
• Experience working with large, complex datasets, including preprocessing, feature engineering, and encoding techniques.
  
  

Key Responsibilities:

• Experiment Design & Analysis: Design, execute, and interpret controlled experiments (e.g., A/B tests, multivariate tests) to evaluate the effectiveness of ML models and strategies.
• Data Analysis & Insights: Conduct exploratory data analysis (EDA), hypothesis testing, and statistical modelling to support ML and business objectives.
• ML Model Support: Assist ML Scientists in preparing data, engineering features, and evaluating models for pricing and personalization solutions.
• Reporting & Visualization: Create dashboards and visualizations to track key metrics, experiment outcomes, and model performance.
• d-Hoc Analysis: Perform deep dives and provide actionable insights on specific datasets or business questions to inform strategic decisions.
• Collaboration: Partner with ML Scientists, Data Engineers, and Product Managers to align on experimentation goals and ensure successful implementation of ML solutions.