Insight Global
Overview
A large global retailer in the Northwest Arkansas area is looking for a Staff Data Scientist Forecasting, Causal Inference & Optimization. Responsibilities
Lead design and deployment of global forecasting models (e.g., TFT, N-BEATS, PatchTST) across complex retail/e-commerce hierarchies. Introduce causal modeling approaches (synthetic controls, panel methods, Granger causality, uplift modeling) to improve decision-making beyond correlations. Apply optimization frameworks (MILP, convex programming, stochastic optimization, reinforcement learning-based policies) for problems such as inventory, pricing, and promo allocation. Build and maintain experimentation pipelines (A/B testing, quasi-experiments, multi-armed bandits) for evaluating causal impacts of interventions. Mentor junior scientists, review research and production code, and ensure reproducibility and scalability in pipelines. Collaborate with engineering to implement forecasting + optimization systems in production (Airflow, Astronomer, Spark/Ray). Act as technical lead on multiple projects, balancing research rigor with business delivery. Company statement
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com. To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements
Master's degree or PHD in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years' experience in analytics related field. Strong foundation in time series forecasting, causal inference, and optimization theory. Hands-on experience with transformer-based models (TFT, PatchTST, GPT variants) and sequence modeling. Familiarity with PEFT methods (LoRA, adapters) for efficiently fine-tuning large architectures. Proficiency in Python, SQL, PyTorch, Spark/Ray, and stats/econometrics libraries. Experience deploying ML systems at scale on cloud platforms (GCP/Azure). Publications or open-source contributions in forecasting, optimization, or causal inference. Exposure to ML observability: drift detection, retraining triggers, and causality-informed monitoring. Background in retail, e-commerce, or operations analytics.
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A large global retailer in the Northwest Arkansas area is looking for a Staff Data Scientist Forecasting, Causal Inference & Optimization. Responsibilities
Lead design and deployment of global forecasting models (e.g., TFT, N-BEATS, PatchTST) across complex retail/e-commerce hierarchies. Introduce causal modeling approaches (synthetic controls, panel methods, Granger causality, uplift modeling) to improve decision-making beyond correlations. Apply optimization frameworks (MILP, convex programming, stochastic optimization, reinforcement learning-based policies) for problems such as inventory, pricing, and promo allocation. Build and maintain experimentation pipelines (A/B testing, quasi-experiments, multi-armed bandits) for evaluating causal impacts of interventions. Mentor junior scientists, review research and production code, and ensure reproducibility and scalability in pipelines. Collaborate with engineering to implement forecasting + optimization systems in production (Airflow, Astronomer, Spark/Ray). Act as technical lead on multiple projects, balancing research rigor with business delivery. Company statement
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com. To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements
Master's degree or PHD in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years' experience in analytics related field. Strong foundation in time series forecasting, causal inference, and optimization theory. Hands-on experience with transformer-based models (TFT, PatchTST, GPT variants) and sequence modeling. Familiarity with PEFT methods (LoRA, adapters) for efficiently fine-tuning large architectures. Proficiency in Python, SQL, PyTorch, Spark/Ray, and stats/econometrics libraries. Experience deploying ML systems at scale on cloud platforms (GCP/Azure). Publications or open-source contributions in forecasting, optimization, or causal inference. Exposure to ML observability: drift detection, retraining triggers, and causality-informed monitoring. Background in retail, e-commerce, or operations analytics.
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