ML Specialist

Talantir; New Jersey; Hybrid; USD 120,000 - 160,000; Full time

Job summary

Talantir is seeking an ML Specialist for a hybrid NJ/NY role, combining theoretical modeling with production deployment. You will develop robust classical ML models for causal inference and apply modern deep learning methods to boost forecasting accuracy.

The position emphasizes building production-grade ML pipelines, ensuring scalability, drift monitoring, and seamless enterprise integration.

Qualifications

• Experience with classical methods (Regression, ARIMA, Prophet) for time-series forecasting.
• Experience applying causal libraries (CausalML, EconML) to determine incrementality.
• Hands-on CI/CD for ML, Docker/Kubernetes, MLflow/Kubeflow to automate training, deployment, and monitoring.

Responsibilities

• Develop production-grade ML models for forecasting and inference.
• Build scalable ML pipelines with MLOps to monitor drift and performance.
• Collaborate with data engineering and product teams to deploy models.

Skills

Advanced Forecasting & AI

Causal Inference & Simulation

MLOps & Engineering

Job description

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,)

Key Skills & Competencies

• 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)