Senior Applied Scientist

Requirements:

• Experience working with machine learning algorithms and technologies, with hands-on production deployment experience.
• Experience working on time series problems, implementing existing methods, and developing new solutions (statistical and probabilistic models; deep learning CNN, LSTM, and transformer architectures).
• PyTorch-based, GPU-accelerated training of time series models, including experience optimizing training pipelines for large-scale, high-cardinality demand data.
• Deep, hands-on understanding of statistical forecasting methods (ARIMA/SARIMA, exponential smoothing, state-space, and Bayesian structural models) and when to apply them versus ML/DL approaches.
• Design, develop, and maintain scalable machine learning systems for time series forecasting and general predictive modeling (on both CPU and GPU machines).
• Strong proficiency in Python and the ability to write production-level code.
• Critical thinking, strong technical knowledge in data structures, algorithms, and system design.

Key Technical Skills:

• Languages/Libs: Python (production-grade); working knowledge of SQL, Pandas, and Polars.
• ML/DL Frameworks: PyTorch, scikit-learn, and time series Python libraries (e. g., statsmodels, GluonTS, sktime, Prophet, and Chronos-2 AutoGluon).
• Cloud & Infrastructure: AWS, GCP, or Azure; containerization and orchestration (Docker, Kubernetes) for training and inference workloads.
• MLOps: Model monitoring, alerting, experiment tracking, and CI/CD pipelines for ML systems in production.
• GPU Computing: Experience with GPU-based distributed training and inference for deep learning models.
• Data Tools: Experience working with large-scale structured/unstructured data pipelines and feature engineering.
• Preferred Skills
• Working knowledge of the latest research directions in time series foundation models DeepAR, Chronos-2 and related pretrained/foundational forecasting architectures and the ability to evaluate and adapt them for production use.
• Experience building deep learning-based time series models that account for promotional and event effects and supporting inventory optimization simulation setups.
• Experience with demand pricing algorithms, including dynamic and elasticity-based pricing approaches.
• Exposure to reinforcement learning (RL)-based algorithms for pricing and inventory optimization.
• Ability to do exploratory data analysis, read research papers and state-of-the-art models in literature, and implement them.
• Potential to innovate novel machine learning methods at industry standards, with preferred publications in top-tier conferences (e. g., NeurIPS, ICML, ICLR, KDD, AAAI).
• Exposure to natural language processing and computer vision algorithms is a bonus.
• Knowledge of data governance and ethical AI principles.