Capacity and Performance Data Scientist
Motorola Solutions
Bengaluru, Karnataka, India
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
