HENOK TEAGHES

Dynamic professional with a proven track record at MuseAf enterprises.CO.LTD, excelling in SQL and demonstrating exceptional teamwork abilities. Expert in enhancing operational efficiency and customer satisfaction, with a knack for data accuracy and creative problem-solving. Skilled in leveraging Agile methodologies to drive project success, achieving significant improvements in data management and visual content creation.

•  Building Websites and applications by writing code (HTML, JS, CSS)

•  Worked independently on website projects and upgrades to ensure quality and ongoing client satisfaction

•  Effectively used WordPress content management system from independently learning its functionality

•  building a personal portfolio, landing webpage and calculator

Initiate with IBM Cognos Analytics to aggregate and analyse sales data from various dealerships, identifying trends in performance and profitability

•  Developed comprehensive dashboards to visualise sales metrics, profit margins, and inventory levels, facilitating data-driven decision-making for dealership management

•  Conducted in-depth profitability analysis per dealer, providing actionable insights to enhance sales strategies and operational efficiency

•  Collaborated with cross-functional teams to refine sales processes based on data insights, driving continuous improvement

• Applied Pandas to analyze Tesla's revenue trends over time and visualize the data using Matplotlib or Seaborn to identify growth patterns and seasonal variations
• Implemented time series forecasting models (e.g., ARIMA, Prophet) in Python to predict Tesla’s future revenue based on historical data
• Analyzed and visualised Tesla's revenue by product categories (e.g., vehicles, energy products) using Python to uncover key revenue drivers

• I gathered historical data on flight delays, including factors such as weather conditions, airport congestion, airline performance, and flight schedules to identify patterns and trends.
• Developed predictive models using machine learning or statistical techniques to estimate the likelihood and duration of flight delays based on various input variables
• Evaluated the economic, operational, and customer service impacts of flight delays, including costs, missed connections, and passenger satisfaction
• Utilize Python's Matplotlib and Seaborn libraries to create charts (e.g., bar plots, histograms, and heatmaps) to visualize patterns in flight delays, such as delay distribution by airline, time of day, or weather conditions.