Senior ML & Time-Series Leader - Forecasting & MLOps

Inxite Out; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Inxite Out seeks a Senior Data Scientist with 6–8+ years of experience to lead AI/ML initiatives and mentor junior data scientists. You will develop enterprise forecasting models and apply both classical and modern techniques to drive business value.

You will design end-to-end pipelines for time-series forecasting, data quality, MLOps, and Azure/AWS integrations, while championing governance, monitoring, and responsible AI across production systems.

Qualifications

• 6-8+ years of experience building and deploying ML models in enterprise environments.
• Strong Python, Spark/SQL with Databricks experience required.
• Proficiency with time-series analysis and NLP tools.

Responsibilities

• Lead design and deployment of enterprise-scale forecasting systems with time-series focus.
• Develop data quality frameworks to handle missing data and outliers in production.
• Create forecasting systems for 24-month demand forecasts using historical patterns and external factors.
• Design interpretable ensemble models to identify demand drivers.
• Build reusable model training and deployment pipelines for consistency.
• Implement monitoring to detect forecast issues and data drift.
• Lead integration and monitoring of LLM-based systems within cloud workflows.
• Establish model governance with monitoring and safety controls.

Skills

Python

PySpark

SQL

Databricks

MLOps

Docker

Git/GitHub

AWS/Azure

Time Series

NLP

Random Forest

Scikit-learn

K-Means

KNN

Linear Regression

Naive Bayes

Job description

Inxite Out seeks a Senior Data Scientist with 6–8+ years of experience to lead AI/ML initiatives and mentor junior data scientists. You will develop enterprise forecasting models and apply both classical and modern techniques to drive business value.

You will design end-to-end pipelines for time-series forecasting, data quality, MLOps, and Azure/AWS integrations, while championing governance, monitoring, and responsible AI across production systems.