Data Scientist & Experimentation Analyst
New
IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonSQLMachine LearningTableauPandasA/B testingscikit-learn
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.
Responsibilities
- 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.
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