|JD- 577 – [22-03-25]|- Shyena Tech Yarns Pvt. Ltd. – Hiring for multiple position

Approx. Package for Internship period for 6 months: Rs.5,000/- Per Month.

Approx. Package after Internship Period: Rs.3,00,000/- per Annum.(Based on internship performance)

Roles &  Responsibilities:

FOR PYTHON/JAVA Developer

1. Software Development
System Design: Full-time developers are responsible for architecting and designing the structure of new systems, software features, or improvements.
Coding : Writing clean, efficient, and maintainable code. In Java or Python, this may involve working with frameworks like Spring, Django, Flask, or others.
Code Optimization: Refactoring existing code for performance improvements or to adhere to evolving standards.
2. Collaboration and Communication
Agile Workflows: Participating in daily stand-ups, sprint planning, and retrospectives to ensure smooth project management and communication with cross-functional teams.
Mentoring: Senior developers may mentor interns or junior developers, helping them understand processes, best practices, and technical challenges.
Cross-team Coordination: Collaborating with product managers, designers, QA engineers, and other stakeholders to ensure product features are being developed as per requirements.
3. Testing and Quality Assurance
Unit Testing & TDD: Writing unit tests, integration tests, and following test-driven development (TDD) practices.
Automation & CI/CD: Setting up and maintaining continuous integration and deployment pipelines to automate testing and release processes.
Bug Fixing : Diagnosing and resolving bugs, often involving complex debugging processes.
4. Maintenance and Support
Code Reviews: Actively participating in code reviews to ensure quality, consistency, and adherence to best practices.
Documentation: Writing and maintaining internal and external documentation for software systems, APIs, and processes.
Performance Monitoring: Identifying and addressing performance bottlenecks in existing applications or services.

FOR DATA SCIENCE

1. Data Collection and Preparation
Data Acquisition: Identifying relevant data sources, scraping data from the web, and connecting to APIs or databases to gather raw data.
Data Wrangling: Cleaning, transforming, and structuring data in preparation for analysis and model training. This includes handling missing values, outliers, and ensuring consistency in the dataset.
Feature Engineering: Creating new features from raw data, improving the predictive power of machine learning models.
2. Exploratory Data Analysis (EDA)
In-depth Analysis: Performing advanced statistical analysis and visual exploration of data to uncover patterns, trends, and outliers.
Data Visualization: Using advanced visualization tools like Matplotlib, Seaborn, Plotly, or Tableau to communicate data insights clearly to stakeholders.
Hypothesis Testing: Applying statistical methods to test assumptions or validate hypotheses about the data.
3. Model Development and Machine Learning
Building Models: Developing machine learning models (regression, classification, clustering, recommendation systems) using advanced techniques (e.g., XGBoost, Random Forest, neural networks).
Model Selection: Selecting the appropriate model(s) based on the problem (e.g., supervised vs. unsupervised learning, deep learning vs. classical ML).
Model Evaluation and Tuning: Evaluating models using performance metrics (e.g., RMSE, ROC, AUC) and tuning hyperparameters to optimize model performance.
Advanced Algorithms: Implementing complex algorithms like deep learning models (e.g., CNNs, RNNs), natural language processing (NLP), and reinforcement learning when needed.