Senior Statistical Modeling & Forecasting Scientist

Job Title: Senior Data Scientist (Forecasting & Time Series)

(or Senior Statistical Modeling & Forecasting Scientist)

About the Role

We are looking for a highly analytical and driven PhD-level Statistician / Data Scientist to join our team and lead advanced statistical modeling and demand forecasting initiatives. This role will play a critical part in building predictive models, generating actionable insights, and supporting data-driven decision-making across the organization.

Key Responsibilities

Develop and deploy advanced statistical models for demand forecasting and business optimization

Perform time series analysis using classical and modern techniques (ARIMA, SARIMA, state space models, etc.)

Build and validate predictive models using regression, machine learning, and statistical techniques

Analyze large datasets to uncover patterns, trends, and actionable insights

Translate complex analytical results into clear business recommendations for stakeholders

Collaborate with cross-functional teams (product, operations, finance, etc.) to drive data-driven strategies

Continuously improve forecasting accuracy and model performance

Design experiments and evaluate model effectiveness using statistical rigor

Document methodologies and ensure reproducibility of models

Required Qualifications

PhD in Statistics (or closely related field such as Mathematics, Econometrics, or Data Science)

34+ years of hands-on experience in statistical modeling and forecasting

Strong expertise in:

Time series analysis

Regression techniques

Predictive modeling

Proficiency in Python and/or R (NumPy, pandas, statsmodels, scikit-learn, etc.)

Strong understanding of statistical theory and its practical application

Experience working with large and complex datasets

Excellent problem-solving and analytical skills

Strong communication skills with the ability to explain technical concepts to non-technical stakeholders

Preferred Qualifications

Experience in demand forecasting, supply chain, retail, or pricing optimization

Familiarity with machine learning frameworks and advanced modeling techniques

Experience with SQL, data pipelines, and cloud platforms (AWS, GCP, Azure)

Exposure to MLOps practices and model deployment

Knowledge of causal inference or experimental design