Optimizing time series data is crucial for forecasting, trend analysis, and anomaly detection. It presents challenges like temporal dependencies, seasonality, and autocorrelation. Traditional models like ARIMA, SARIMA, and ETS are suitable for simple data, while machine learning models like lightgbm,Xgboost and LSTMs are preferred for complex data.

• Gather data from various sources, including internal databases, external datasets, and Apis. And Ensure data quality and integrity through proper cleaning, preprocessing, and validation techniques• Perform exploratory data analysis (EDA) to uncover trends, patterns, and insights and use statistical methods to analyze data and derive actionable insights that inform business strategies• Develop, implement, and maintain machine learning models to solve complex business problems and use predictive modeling techniques to forecast trends, customer behavior, and business outcomes• Create visualizations and dashboards to communicate findings to stakeholders in a clear and compelling manner and prepare detailed reports and presentations that summarize analysis results and recommendations.• Work closely with product managers, engineers, and other stakeholders to understand business needs and translate them into data-driven solutions.

• identify inefficiencies and recommend process improvements, optimizing aerospace operations and reducing costs,• support the development of more reliable and efficient maintenance schedules and operational procedure by analyzing data related to component failures and maintenance needs and help improve the safety and reliability of aerospace operations• provide critical insights that drive strategic decision-making in aerospace projects and initiatives by using methods such as reliability analysis, regression analysis, hypothesis testing, and ANOVA to understand trends and patterns in aerospace operations and engineering.• Develop predictive models to forecast maintenance needs, component failures, and operational performance and create detailed reports and visualizations to communicate findings to aerospace engineers, management, and other stakeholders.

• Design and implement methods to collect and manage data from various sources and ensure data quality and integrity through proper cleaning, preprocessing, and validation techniques.• Perform exploratory data analysis (EDA) to uncover trends, patterns, and insights and apply statistical methods such as regression analysis, hypothesis testing, ANOVA, and others to analyze data and derive meaningful insights.• transform data into valuable insights that support informed decision-making, enhance client experiences, and drive business success.

• Collect, analyze and interpret qualitative and quantitative data with statistical theories and methods using Excel• Predictive modeling like classification and regression analysis using SPSS and Real stats in Excel

• Collect, analyze and interpret qualitative and quantitative data with statistical theories and methods using Excel• Apply statistical methods such as regression analysis, hypothesis testing, ANOVA, and others to analyze data and derive meaningful insights