Overview:
JD
Feature Selection, Feature Engineering, Statistical Modeling, Model Building, Machine Learning, Python, SQL, NLP, Data Engineering, ETL/ELT, Data Pipeline Development.
Key Responsibilities
1. Lead feature selection initiatives using advanced statistical techniques such as correlation analysis, hypothesis testing, regression analysis, Information Value (IV), Weight of Evidence (WoE), PCA, and feature importance methods.
2. Analyze large and complex datasets to identify, evaluate, and prioritize key variables that significantly influence business outcomes and predictive performance.
3. Design, develop, and maintain scalable feature engineering frameworks for both structured and unstructured data sources.
4. Perform exploratory data analysis (EDA), data profiling, and statistical validation to uncover meaningful patterns, relationships, and predictive features.
5. Utilize Python and SQL to extract, transform, analyze, and validate data while ensuring data quality and consistency across analytical workflows.
6. Build, train, validate, and optimize Machine Learning models using appropriate algorithms and techniques to solve business problems and improve predictive accuracy.
7. Apply Machine Learning algorithms to assess feature effectiveness, validate feature sets, and improve model accuracy, robustness, and interpretability.
8. Leverage NLP techniques to extract, engineer, and optimize features from textual data for downstream analytics and predictive modeling.
9. Collaborate closely with business stakeholders, product teams, and data engineers to translate business requirements into meaningful analytical features, predictive models, and actionable insights.
10. Build, optimize, and productionize data pipelines and feature datasets, working with Data Engineering teams to ensure scalability, efficiency, governance, and operational readiness.
11. Conduct Data Engineering handover activities by documenting data pipelines, feature engineering logic, model inputs/outputs, transformation rules, and deployment requirements to ensure seamless transition to engineering and operations teams.
12. Support model deployment, monitoring, performance tracking, and continuous improvement by partnering with Data Engineering and MLOps teams.
13. Document feature selection methodologies, model development processes, statistical findings, assumptions, and recommendations while establishing best practices for reusable and automated analytics solutions.
Required Skills
• 8+ years of experience in Data Science, Analytics, or Machine Learning.
• Strong expertise in Feature Selection, Feature Engineering, Statistical Modeling, and Predictive Modeling.
• Deep understanding of Statistics, Hypothesis Testing, Regression Analysis, Feature Importance Techniques, and Dimensionality Reduction.
• Hands-on experience in Machine Learning model development, validation, tuning, and evaluation.
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels).
• Strong SQL skills with experience writing complex queries and performing large-scale data analysis.
• Experience with NLP techniques, text processing, and feature extraction.
• Knowledge of Data Engineering concepts, ETL/ELT pipelines, data transformation, and production data workflows.
• Experience with model deployment, MLOps concepts, and collaboration with Data Engineering teams.
• Excellent analytical, problem-solving, documentation, and stakeholder communication skills.
Skills:
NLP,HYPOTHESIS TESTING,SQL,PYTHON,PREDICTIVE MODELING,DATA TRANSFORMATION,NUMPY,STATISTICS,SQL SKILLS,REGRESSION ANALYSIS
