Data Scientist & Experimentation Analyst

Job Summary:

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.
• Ad-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.