HCL Unica Product_Lead Python Developer_Pune/Mumbai

Greetings from HCL Software Is a Product Development Division of HCL Tech!!

hcl-software.com

About Unica Product: -

We are Seeking a Lead Python Developer (Data Science, AI/ML) with 8+ Yrs of Strong Data Science and Machine Learning skills and experience to deliver AI driven Marketing Campaigns.

Qualifications & Skills: -

Mandatory Python Development Experience.

1. Data Ingestion, ETL - Apache Airflow, Kafka.

2. APIs - REST, Postman

Good to have: -

1. Databases - SQL Alchemy.
2. Machine Learning - Pandas, NumPy, Scikit-learn, Matplotlib.
3. GenAI (LLM+RAG) - LangChain, Llama Index, HayStack.

Responsibilities: -

1. Python Programming & Libraries: Proficient in Python with extensive experience using Pandas for data manipulation, NumPy for numerical operations, and Matplotlib/Seaborn for data visualization.
2. Statistical Analysis & Modelling: Strong understanding of statistical concepts, including descriptive statistics, inferential statistics, hypothesis testing, regression analysis, and time series analysis.
3. Data Cleaning & Preprocessing: Expertise in handling messy real-world data, including dealing with missing values, outliers, data normalization/standardization, feature engineering, and data transformation.
4. SQL & Database Management: Ability to query and manage data efficiently from relational databases using SQL, and ideally some familiarity with NoSQL databases.
5. Exploratory Data Analysis (EDA): Skill in visually and numerically exploring datasets to understand their characteristics, identify patterns, anomalies, and relationships.
6. 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).
7. 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.
8. 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.
9. 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.

Travel:

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Compensation: