Python Developer

We are Seeking a Lead Python Developer (ETL) with 7+ Yrs of Strong Exp in Data Science and Machine Learning skills and experience to deliver AI driven Marketing Campaigns.

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.
  

Responsibilities: -

• Python Programming & Libraries: Proficient in Python with extensive experience using Pandas for data manipulation, NumPy for numerical operations, and Matplotlib/Seaborn for data visualization.
  
• Statistical Analysis & Modelling: Strong understanding of statistical concepts, including descriptive statistics, inferential statistics, hypothesis testing, regression analysis, and time series analysis.
  
• Data Cleaning & Preprocessing: Expertise in handling messy real-world data, including dealing with missing values, outliers, data normalization/standardization, feature engineering, and data transformation.
  
• SQL & Database Management: Ability to query and manage data efficiently from relational databases using SQL, and ideally some familiarity with NoSQL databases.
  
• Exploratory Data Analysis (EDA): Skill in visually and numerically exploring datasets to understand their characteristics, identify patterns, anomalies, and relationships.
  
• Machine Learning Algorithms: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).
  
• Deep Learning Frameworks: 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.
  
• Model Evaluation & Optimization: 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.
  
• Deployment & MLOps Concepts: Understanding of how to deploy machine learning models into production environments, including concepts of API creation, containerization (Docker), version control for models, and monitoring.
  

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