Shyena Tech Yarns Pvt Limited is a Data Science and Digital Transformation company, headquartered in Pune, India. We provide Artificial Intelligence, Machine Learning, Big Data and Data Centric Platform Development solutions to our customers. Our Platforms, Inteliconvo is an AI Powered Speech Analytics platform to improve Sales & Collections. The Acceler8CX platform uses AI at it’s core to improve Customer Experience. Our Workmate platform is based on deep learning models and can be used to automate complex business workflow. In nut & shell, Shyena Tech Yarns primarily provides data centric services to the clients.
Position: Intern
Role:
1.AI/Data Science
2. Python / Django Developers.
3. DevOps Engineer
Experience:Fresher
Job Location: Pune
ELIGIBILITY CRITERIA:
Qualification: B.E/B.Tech (CS/IT)
Pass out Year For DevOps Role: 2022 and earlier pass-outs
Pass out Year For AI/Data Science and Data Engineer Role: 2023 and earlier pass-outs
Percentage Criteria: NA
Any Bond: 2.6 Years (including the internship period (total 30 months from joining))
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)
Data Science Interns – Role & Responsibilities
* Work on data engineering, data visualization, and data science tasks. * Build deep learning models using Generative AI, Computer Vision, and NLP. * Apply ML and deep learning algorithms effectively. * Work with data visualization tools like Tableau or Power BI. * Perform data processing using Pandas and Spark. * Possess strong analytical and problem-solving skills.
Skills Required
1. Programming Skills 1. Python: The most widely used language in data science for its rich ecosystem of libraries (e.g., NumPy, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn). 2. R: Especially useful in statistical analysis and visualization. 3. SQL: Strong ability to query databases, including writing complex joins, aggregations, and subqueries. 4. Scripting: Familiarity with bash scripting for automating tasks, data processing, and handling files. 2. Mathematics and Statistics 1. Probability Theory: Knowledge of probability distributions, Bayesian thinking, and statistical inference. 2. Linear Algebra: Essential for machine learning, especially in deep learning (e.g., matrix operations). 3. Calculus: Used in understanding optimization algorithms, such as gradient descent. 4. Statistical Methods: Hypothesis testing, p-values, confidence intervals, correlation, and regression analysis. 5. Time Series Analysis: Analyzing and forecasting data that varies over time (e.g., ARIMA, Seasonal Decomposition).
Roles and Responsibility – Python / Django Engineer
1. Build and maintain web applications using Python and Django 2. Develop RESTful APIs with Django Rest Framework 3. Work with databases (PostgreSQL/MySQL) and Django ORM 4. Write clean, efficient, and testable code 5. Deploy and maintain applications on cloud platforms (e.g., AWS, Heroku) 6. Collaborate with front-end developers, designers, and product teams 7. Debug, optimize, and ensure application security 8. Participate in code reviews and maintain documentation
Skills Required:
1. Exceptional programming skills in Python, Spark, Kafka, Pyspark, and C++ 2. Strong SQL and complex query writing skills 3. Knowledge of Pandas, Numpy, and Databricks (advantageous) 4. Familiarity with Exploratory Data Analysis and data pre-processing
Roles and Responsibility of DevOps Engineer:
1. CI/CD Pipeline Management – Design, implement, and maintain Continuous Integration and Continuous Deployment pipelines. 2. Infrastructure Automation – Use tools like Terraform, Ansible, or CloudFormation to automate infrastructure provisioning. 3. Monitoring & Incident Response – Set up system monitoring, logging, and alerting; respond to and resolve incidents efficiently. 4. Collaboration & Communication – Work closely with development, QA, and operations teams to streamline the software delivery process.
Skills Required: 1. Proficiency in tools like Jenkins, Docker, Kubernetes, Git, and cloud platforms (AWS, Azure, GCP). 2. Scripting knowledge (Bash, Python, etc.) for automation. 3. Strong understanding of Linux/Unix systems. 4. Experience with Infrastructure as Code (IaC) and configuration management tools.
