Data Scientist

The Data Scientist role involves driving end-to-end data science projects to support data-driven decision-making. The candidate will apply advanced machine learning techniques and collaborate with stakeholders to address business requirements, preferably within the telecom industry.

Job Responsibilities

1. Lead all stages of the data science project life cycle, from data exploration to deployment.
2. Collaborate with stakeholders to gather and define business requirements, creating clear business requirement documentation.
3. Present progress, insights, and recommendations to management on various use cases.
4. Conduct data wrangling, cleaning, and feature engineering to prepare datasets for analysis.
5. Develop and optimize machine learning models using tools like Scikit-Learn, XGBoost, and LightGBM.
6. Work with big data technologies, including Spark (pySpark) and Hadoop, to handle large datasets.
7. Build APIs using frameworks such as FastAPI, Flask, or Django for model integration.
8. Utilize deep learning libraries, including TensorFlow and PyTorch, for projects in computer vision and NLP.
9. Manage model tracking and monitoring to ensure consistent performance.
10. Apply containerization tools (e.g., Docker, Kubernetes, OpenShift) for deployment.
11. Leverage cloud services, preferably Azure, for scalable data processing.
12. Utilize software engineering tools, including Git and Jenkins, for version control and automation.
13. Stay updated with advancements in AI, large language models, and chatbot technologies (e.g., ChatGPT).

Qualifications and Skills:

1. Education: Bachelor’s degree in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, or a related field; Master’s degree preferred.
2. Experience: Minimum of 6 years in data science or related roles, ideally in the telecom industry.
3. Technical Skills:
4. Proficiency in Python and libraries such as Numpy, Pandas, Scikit-Learn, Matplotlib, Seaborn, and Plotly.
5. Strong SQL skills and expertise in data wrangling and feature engineering.
6. Experience with deep learning (TensorFlow, PyTorch), containerization (Docker, Kubernetes), and cloud platforms (Azure).
7. Familiarity with big data (Spark, Hadoop) and software engineering tools (Git, Jenkins).
8. Certifications: A Data Science Professional Certificate is preferred.