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Key Responsibilities
Time Series Analysis: Analyze and preprocess time series data to identify patterns, seasonality, and trends using statistical methods, feature engineering, and data visualization.
Model Development: Develop, test, and deploy state-of-the-art forecasting models, such as ARIMA, Exponential Smoothing, Prophet, LSTM, or other deep learning architectures to generate reliable predictions.
Data Integration: Collaborate with data engineers to ensure the integration of clean and relevant data for modeling and maintain data pipelines for ongoing forecasting tasks.
Model Evaluation: Evaluate model performance through metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and implement strategies for model improvement.
Feature Selection: Select and engineer relevant features to enhance the forecasting models, leveraging domain knowledge and advanced feature extraction techniques.
Automation: Develop automated pipelines for time series forecasting and implement continuous improvement in forecasting accuracy and efficiency. Visualization: Communicate forecasting results effectively through data visualization tools and dashboards for stakeholders, making the insights actionable. Collaboration: Collaborate with cross-functional teams, including business analysts, engineers, and domain experts, to understand forecasting requirements and drive decision-making based on insights.
Research and Innovation: Stay up to date with the latest developments in time series forecasting and contribute to the research and development of new forecasting techniques and tools.
Documentation: Maintain clear and concise documentation of modeling techniques, data sources, and results for knowledge sharing and reproducibility.
Requirements
- Proven experience in time series forecasting, with a minimum of 5 years in Data Science, Statistics, Mathematics, or related roles.
- Strong proficiency in Python and data science libraries, such as pandas, NumPy, scikit-learn, and TensorFlow or PyTorch.
- Expertise in time series forecasting techniques and machine learning algorithms.
- Familiarity with database systems, data manipulation, and ETL processes.
- Excellent problem-solving skills and the ability to work with large, complex datasets.
- Strong communication and data visualization skills.
- Experience with version control and collaborative development tools (e.g., Git).
- Strong analytical and critical thinking skills with a detail-oriented mindset.
- Knowledge of cloud platforms and tools (e.g., AWS, Azure, Google Cloud) is a plus.
If you like wild growth and working with happy, enthusiastic over-achievers, you’ll enjoy your career with us!
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