Lead Data Scientist

About the job

Role Overview: As the Lead Data Scientist, you will be responsible for leading the development and implementation of time series forecasting models. Your expertise will play a crucial role in guiding the team to create solutions that impact critical business decisions and enhance predictive accuracy. Key Responsibilities: - Lead the design, development, and implementation of advanced time series forecasting models. - Manage and mentor a team of data scientists, providing guidance and support. - Collaborate with cross-functional teams to understand business requirements and data sources. - Research and implement innovative forecasting techniques to improve predictive accuracy. - Develop and maintain documentation for models and processes. - Present findings and recommendations to stakeholders in a clear and concise manner. - Ensure the robustness and scalability of forecasting models. - Identify and resolve data quality issues that impact model performance. - Stay up-to-date with the latest advancements in time series forecasting and machine learning. - Contribute to the development of best practices and standards for data science projects. Qualifications Required: - Masters or Ph.D. in Statistics, Mathematics, Computer Science, or a related field. - 5 years of experience in data science with a focus on time series forecasting. - Proficiency in Python and R, with expertise in libraries such as pandas, scikit-learn, and statsmodels. - Experience with various time series forecasting techniques, including ARIMA, Exponential Smoothing, and Prophet. - Strong understanding of statistical analysis and machine learning concepts. - Experience with cloud computing platforms such as AWS, Azure, or GCP. - Excellent communication and leadership skills. - Ability to work independently and as part of a team. - Familiarity with version control systems such as Git. - Experience with database management systems such as SQL or NoSQL.