Responsibilities

• Conducting capacity planning, monitoring and performance analysis for systems and applications, utilizing data science methodologies and tools.
• Collecting, analyzing, and interpreting performance data to identify system bottlenecks, resource utilization patterns, and areas for optimization.
• Develop and maintain performance metrics, dashboards, and reports to provide actionable insights and drive improvements
• Collaborating with cross-functional teams, including Operations team, developers, and business stakeholders, to identify performance requirements and ensure systems meet performance goals.
• Utilizing statistical modeling, machine learning, and data visualization techniques to analyze and present performance data in a clear and actionable manner.
• Conduct capacity planning to ensure system scalability, considering factors such as user growth, application updates, and infrastructure changes.
• Identifying and recommending improvements to system architecture, configurations, and resource allocation to optimize system capacity and performance.
• Participate in performance testing efforts, including load, stress, and endurance tests, to validate system performance and stability
• Conducting root cause analysis and troubleshooting performance issues, working closely with technical teams to resolve problems and implement performance enhancements.
• Staying updated with the latest industry trends and advancements in data science, capacity planning, and performance optimization techniques.

Basic Requirements

• Bachelor's or Master's degree in computer science, data science, or a related field.
• Minimum of 6+ years of experience in Data science.
• Strong programming skills in languages like Python or R, along with expertise in data manipulation, data cleaning, and feature engineering. Knowledge of SQL for data extraction and database querying.
• Familiarity with statistical modeling and data mining techniques for performance analysis including hypothesis testing, regression analysis, time series analysis, and clustering.
• Understanding and experience with machine learning algorithms and techniques such as classification, regression, clustering, dimensionality reduction, and ensemble methods. Familiarity with popular machine learning libraries and frameworks such as scikit-learn, TensorFlow, or PyTorch
• Hands-on experience with performance monitoring and analysis tools, such as Grafana, Prometheus, ELK Stack, Nagios, Zabbix etc.
• Ability to effectively visualize and communicate complex data insights to both technical and non-technical stakeholders. Proficiency in data visualization tools like Tableau, Power BI, or matplotlib/seaborn in Python.
• Experience in collecting, cleaning, and preprocessing large datasets from various sources. Knowledge of data cleaning techniques, dealing with missing values, outlier detection, and handling unstructured data.
• Familiarity with distributed computing frameworks like Apache Hadoop, Spark, or Hive, and experience working with large-scale datasets and distributed computing environments. Understanding of cloud-based data platforms like AWS, Azure, or Google Cloud.
• Strong problem-solving and analytical skills, with the ability to interpret complex data and identify performance improvement opportunities.
• Prior experience in capacity planning, performance analysis, or system administration is highly desirable.
• Certifications in data science, performance analysis, or related areas can be a plus, but are not mandatory
• Excellent communication and interpersonal skills, with the ability to present technical information to non-technical audiences.
• Familiarity with relevant industry MCPTT standards and protocols, such as 3GPP, is a plus.
• Awareness of ethical considerations and legal regulations related to data privacy, security, and confidentiality. Knowledge of best practices for handling sensitive data and ensuring compliance with relevant data protection laws