ML Specialist

TLDR

Develop robust classical ML models for causal inference and modern Deep Learning methods to enhance forecasting accuracy, while ensuring production-grade MLOps capabilities.

Role : ML Specialist

Location: NJ/NY (Hybrid)

Role Description A dual-threat technical role responsible for both the theoretical modeling and the engineering required to deploy it. This individual must be capable of building robust classical ML models for causal inference while applying modern Deep Learning methods to enhance forecasting accuracy. Crucially, they must possess strong MLOps capabilities to build production-grade pipelines, ensuring models are scalable, monitored for drift, and integrated seamlessly into the enterprise platform.

Key Skills & Competencies (relevant skills, not expecting the same person to possess all of it, we need 2 profiles and so we can build a mix i.e., 1x Classical ML specialist + 1x Modern MLOps Engineer,)

Advanced Forecasting & AI: Mastery of classical methods (Regression, ARIMA, Prophet) combined with modern Deep Learning architectures for time-series forecasting (LSTMs, Temporal Fusion Transformers, N-BEATS).
Causal Inference & Simulation: Experience applying causal libraries (CausalML, EconML) to determine incrementality and simulate business outcomes under uncertainty.
MLOps & Engineering: Strong hands-on experience with the modern ML stack (CI/CD for ML, Docker/Kubernetes, MLflow/Kubeflow) to automate training, deployment, and monitoring pipelines.
External Job Titles to Search: Senior Machine Learning Engineer (FinTech/Risk), Full-Stack Data Scientist, Applied Scientist (Forecasting & Optimization), AI Engineer (Time-Series focus).