What is your mission? 

You will provide the best service to our partner brands by performing these tasks:

• Design and implement end-to-end forecasting pipelines: data ingestion and preprocessing, model training and validation, automated forecast generation, uncertainty quantification with prediction intervals, and performance monitoring with model recalibration.
• Develop robust statistical and mathematical models: time series decomposition and trend analysis; regression and causal modelling; probabilistic forecasting and interval estimation; seasonality detection and structural change identification; ensemble modelling and model selection.
• Implement forecast evaluation using WAPE, MAPE, RMSE, MAE, bias, forecast drift, prediction interval coverage, and stability across time horizons.
• Perform data signal analysis: correlation and lag analysis, feature engineering and regressor testing, anomaly detection and outlier management, and decomposition of predictable vs. unpredictable variation.
• Translate operational questions into modelling problems; explain forecast behavior, uncertainty, and limitations; present findings clearly and guide teams on model interpretation.

Who are we looking for?

• Education: Bachelor’s degree in Applied Mathematics, Statistics, Data Science, Operations Research, Physics, Econometrics, or Engineering (quantitative focus); Master’s degree is a plus.
• Experience (measurable by years): 0–5 years in forecasting, predictive modelling, time series analysis, or statistical modelling; experience contributing to end-to-end forecasting pipelines (ingestion → preprocessing → modelling → validation → automated generation); exposure to client-facing work and maintaining forecasting workflows (monitoring, recalibration) is an advantage.
• Specific skills/background: Proficiency with forecasting frameworks (Prophet, ARIMA, ETS); Python or R with Pandas, NumPy, dplyr; visualization and exploratory analysis tools (Matplotlib, Seaborn, Plotly, ggplot); version control (Git/GitHub) and reproducible analysis; time series expertise (trend & seasonality modelling, interval estimation & uncertainty quantification, structural breaks, ensemble/model selection); model evaluation (WAPE, MAPE, RMSE, MAE, bias/drift, prediction interval coverage); feature engineering & regressor testing; anomaly detection & outlier management; scalable data pipelines and modelling workflows; exposure to machine learning is beneficial.
• Soft skills: Clear communication, structured reasoning, curiosity-driven problem solving, intellectual honesty, and a collaborative mindset with stakeholders and cross-functional teams.