Senior Time Series Decomposition Specialist

Job Title:

Senior Time Series Decomposition Specialist

Location: United Arab Emirates (Remote)

Employment Type: Full-Time

Work Arrangement: 100% Remote — Work From Home

Level: Senior

We are seeking an experienced Senior Time Series Decomposition Specialist to analyze complex time-dependent datasets and separate underlying patterns into meaningful components, including trend, seasonality, cyclical movements, and irregular variations. The successful candidate will develop and validate decomposition methodologies, improve forecasting reliability, and generate insights that support strategic planning and data-driven decision-making. This fully remote role requires advanced statistical expertise, strong programming capabilities, and the ability to communicate complex analytical findings to stakeholders.

• Design, develop, and implement time series decomposition methodologies for structured and unstructured temporal datasets.
• Separate time-dependent observations into trend, seasonal, cyclical, and residual components.
• Apply classical decomposition, STL, X-13
  
  ARIMA-SEATS, and other appropriate statistical techniques.
• Evaluate additive and multiplicative decomposition approaches based on data characteristics.
• Analyze seasonal patterns, long-term trends, recurring cycles, and irregular fluctuations.
• Identify structural breaks, outliers, anomalies, and changes in underlying time series behavior.
• Assess the impact of calendar effects, holidays, external variables, and exceptional events on observed patterns.
• Develop preprocessing methods to address missing observations, inconsistent intervals, and irregular sampling.
• Validate decomposition outputs using statistical diagnostics and residual analysis.
• Evaluate stationarity, autocorrelation, heteroscedasticity, and other relevant statistical properties.
• Integrate decomposition outputs into forecasting, anomaly detection, and predictive modeling workflows.
• Conduct sensitivity analysis to assess the stability of decomposition results under different assumptions.
• Collaborate with data scientists, forecasting teams, economists, engineers, and business stakeholders.
• Develop reusable analytical frameworks, automated workflows, and quality-control procedures.
• Prepare technical reports, visualizations, and presentations explaining decomposition results and limitations.
• Maintain comprehensive documentation of methodologies, assumptions, validation procedures, and model versions.

• Bachelor's or master's degree in Statistics, Mathematics, Data Science, Econometrics, Economics, or a related quantitative discipline.
• Significant professional experience in time series analysis, statistical modeling, forecasting, or quantitative research.
• Advanced understanding of time series decomposition, trend estimation, seasonal adjustment, and residual analysis.
• Practical experience with STL, classical decomposition, and other established decomposition methodologies.
• Strong knowledge of statistical concepts, including stationarity, autocorrelation, regression, and time-dependent structures.
• Proficiency in Python or R and relevant statistical libraries.
• Advanced SQL skills and experience working with large temporal datasets.
• Experience identifying structural breaks, anomalies, seasonal variations, and cyclical patterns.
• Familiarity with ARIMA-based forecasting, exponential smoothing, and related time series models.
• Ability to evaluate statistical assumptions and validate analytical outputs.
• Experience with data visualization and analytical reporting tools.
• Strong analytical reasoning, problem-solving, and attention to detail.
• Excellent written and verbal communication skills.
• Ability to manage complex analytical projects independently in a fully remote environment.

• 100% remote working arrangement within the stated employment framework.
• Competitive compensation based on qualifications and professional experience.
• Opportunities to work on advanced statistical modeling and forecasting projects.
• Professional development and continuous learning opportunities.
• Collaborative environment with international and multidisciplinary teams.