Lead Python Developer

As a Lead Python Developer (ETL) at HCL Software, you will be responsible for leveraging your 7+ years of strong experience in Data Science and Machine Learning to deliver AI-driven Marketing Campaigns. Here is a summary of your role, key responsibilities, and the qualifications & skills required for this position: **Role Overview:** As a Lead Python Developer (ETL) at HCL Software, your primary responsibility will be to deliver AI-driven Marketing Campaigns by utilizing your expertise in Data Science and Machine Learning. **Key Responsibilities:** - Proficient in Python with extensive experience using Pandas for data manipulation, NumPy for numerical operations, and Matplotlib/Seaborn for data visualization. - Strong understanding of statistical concepts, including descriptive statistics, inferential statistics, hypothesis testing, regression analysis, and time series analysis. - Expertise in handling messy real-world data, including dealing with missing values, outliers, data normalization/standardization, feature engineering, and data transformation. - Ability to query and manage data efficiently from relational databases using SQL, and ideally some familiarity with NoSQL databases. - Skill in visually and numerically exploring datasets to understand their characteristics, identify patterns, anomalies, and relationships. - In-depth knowledge and practical experience with a wide range of ML algorithms such as linear models, tree-based models (Random Forests, Gradient Boosting), SVMs, K-means, and dimensionality reduction techniques (PCA). - Proficiency with at least one major deep learning framework like TensorFlow or PyTorch. This includes understanding neural network architectures (CNNs, RNNs, Transformers) and their application to various problems. - Ability to select appropriate evaluation metrics (e.g., precision, recall, F1-score, AUC-ROC, RMSE) for different problem types, diagnose model performance issues (bias-variance trade-off), and apply optimization techniques. - Understanding of how to deploy machine learning models into production environments, including concepts of API creation, containerization (Docker), version control for models, and monitoring. **Qualifications & Skills:** - Mandatory Python Development Experience. - Data Ingestion, ETL - Apache Airflow, Kafka. - Rest APIs, Postman. - Good to have: Databases - SQL Alchemy. Machine Learning - Pandas, NumPy, Scikit-learn, Matplotlib. GenAI (LLM+RAG) - LangChain, Llama Index, HayStack. In addition to the above details, the job involves approximately 30% travel and is based in India (Pune preferred). The compensation includes a base salary plus bonus. This information is provided by HCL Software, a Product Development Division of HCL Tech, known for delivering transformative software solutions across AI, Automation, Data & Analytics, Security, and Cloud. As a Lead Python Developer (ETL) at HCL Software, you will be responsible for leveraging your 7+ years of strong experience in Data Science and Machine Learning to deliver AI-driven Marketing Campaigns. Here is a summary of your role, key responsibilities, and the qualifications & skills required for this position: **Role Overview:** As a Lead Python Developer (ETL) at HCL Software, your primary responsibility will be to deliver AI-driven Marketing Campaigns by utilizing your expertise in Data Science and Machine Learning. **Key Responsibilities:** - Proficient in Python with extensive experience using Pandas for data manipulation, NumPy for numerical operations, and Matplotlib/Seaborn for data visualization. - Strong understanding of statistical concepts, including descriptive statistics, inferential statistics, hypothesis testing, regression analysis, and time series analysis. - Expertise in handling messy real-world data, including dealing with missing values, outliers, data normalization/standardization, feature engineering, and data transformation. - Ability to query and manage data efficiently from relational databases using SQL, and ideally some familiarity with NoSQL databases. - Skill in visually and numerically exploring datasets to understand their characteristics, identify patterns, anomalies, and relationships. - In-depth knowledge and practical experience with a wide range of ML algorithms such as linear models, tree-based models (Random Forests, Gradient Boosting), SVMs, K-means, and dimensionality reduction techniques (PCA). - Proficiency with at least one major deep learning framework like TensorFlow or PyTorch. This includes understanding neural network architectures (CNNs, RNNs, Transformers) and their application to various problems. - Ability to select appropriate evaluation metrics (e.g., precision, recall, F1-score, AUC-ROC, RMSE) for different problem types, diagnose model performance issues (bias-variance trade-off), and apply optimization techniques. - Understanding of how to deploy machine learning models into production environments, including concepts of