Senior ML Lead - Time Series & NLP Solutions

Sapsf; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Sapsf is seeking a Senior Technical Lead for Traditional ML to lead development, optimization, and deployment of advanced ML and NLP solutions focusing on time series forecasting. You will leverage Python, distributed data processing, and robust model evaluation within the development team.

The role requires deep hands-on experience with Python stack (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM), Spark, Kafka, Airflow, and NLP tools (NLTK, SpaCy) to deliver scalable,

Qualifications

• Advanced proficiency in machine learning and NLP with forecasting experience.
• Strong Python programming with numpy, pandas, scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM.
• Deep knowledge of distributed data processing with Spark and real-time data with Kafka.
• Solid ML model evaluation skills including cross-validation and metrics optimization.
• Experience with workflow orchestration using Airflow and big data platforms.
• Proficiency with NLP libraries NLTK and SpaCy.

Responsibilities

• Develop and implement time series forecasting models using Python and ML libraries.
• Integrate and process large-scale streaming data with Kafka and Spark for real-time training.
• Evaluate ML models with cross-validation, ROC/AUC, PR, F1, and confusion matrices.
• Apply NLP techniques with NLTK and SpaCy to extract relevant features for forecasts.
• Optimize deployment pipelines using Airflow and Hadoop for efficient workflows.
• Collaborate within the team to ensure quality, best practices, and code standards.

Skills

ML & NLP expertise

Python stack

Spark & Kafka

Model evaluation

Airflow

NLTK & SpaCy

Job description

Sapsf is seeking a Senior Technical Lead for Traditional ML to lead development, optimization, and deployment of advanced ML and NLP solutions focusing on time series forecasting. You will leverage Python, distributed data processing, and robust model evaluation within the development team.

The role requires deep hands-on experience with Python stack (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM), Spark, Kafka, Airflow, and NLP tools (NLTK, SpaCy) to deliver scalable,