Data Scientist & ML developer

Key Responsibilities

Data Collection & Analysis

• Gather and analyze data from diverse source systems (databases, spreadsheets, etc.).
• Ensure data is clean, accurate, complete, and ready for prediction tasks.
• Analyze datasets to uncover patterns, correlations, anomalies, and business insights.

Model Development & Deployment

• Design and develop ML models (supervised, unsupervised, reinforcement learning, etc.) based on business requirements (e.g., customer recommendations).
• Train, test, and validate models using real-world data.
• Deploy machine learning models into production environments and monitor their performance.

Visualization & Communication

• Present insights and outcomes using charts, graphs, dashboards (e.g., Matplotlib).
• Collaborate with customers and local teams to understand problems, define requirements, and communicate results effectively.

Technical Execution

• Design and develop Python programs and FastAPI-based microservices.
• Work across Linux and Windows platforms for development and deployment.
• Maintain strong code quality, documentation, and testing procedures.
• Debug, optimize, and enhance existing ML models and services.

Collaboration & Mentoring

• Act as a mentor and technical guide to team members.
• Work in tandem with customer-facing teams to ensure solution alignment and success.
• Follow professional service lifecycle processes including scoping, requirements gathering, development, QA/testing, and deployment.

Required Qualifications & Skills

Education

• Bachelor's or Master's degree in Computer Science, ECE, EEE, EI, IT, or a related field.

Experience

• 4 to 8 years of software consulting experience in enterprise software solutions, including direct customer interaction.
• Hands-on experience with machine learning in real use cases, deployed in commercial systems.

Technical Skills

• Proficiency in Python, PostgreSQL, SQL.
• Expertise in Pandas, NumPy, Scikit-learn, and FastAPI.
• Experience with data transformation, pattern recognition, and feature engineering.
• Strong knowledge of machine learning algorithms:
• Regression, Decision Trees, Random Forests, Bayesian Methods, Neural Networks, etc.
• Ability to choose appropriate algorithms based on use cases.
• Knowledge of statistics, probability, and linear algebra for model building.
• Experience developing on both Linux and Windows platforms.
• Strong design, implementation, debugging, and troubleshooting skills.

Domain Expertise

• Experience in Telecom Software Domain, especially OSS and Configuration Management (CM).

Soft Skills

• Strong written and verbal communication skills.
• Ability to work independently and in collaborative, cross-functional teams.
• Strong mentoring and team leadership capabilities.