Senior Technical Lead - Traditional ML

Sapsf; United States; Vor Ort; USD 150.000 - 210.000; Vollzeit

Zusammenfassung

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,

Qualifikationen

• 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.

Aufgaben

• 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.

Kenntnisse

ML & NLP expertise

Python stack

Spark & Kafka

Model evaluation

Airflow

NLTK & SpaCy

Jobbeschreibung

Career Opportunities: Senior Technical Lead - Traditional ML (157830)

Requisition ID157830-Posted10/12/2026

This role is accountable for developing, optimizing, and deploying advanced machine learning and natural language processing solutions focused on time series forecasting. The individual applies solid expertise in Python, distributed data processing, and ML model evaluation to deliver complex predictive models, ensuring technical excellence and successful project outcomes within the development team.

Key Responsibilities

1. Develop and implement time series forecasting models using Python, scikit-learn, TensorFlow, and StatsModels, optimizing for accuracy and scalability.
2. Integrate and process large-scale streaming data with Apache Kafka and Spark, enabling real-time model training and inference.
3. Evaluate machine learning models using cross-validation, ROC/AUC, Precision/Recall, F1-score, and confusion matrix to ensure robust predictive performance.
4. Apply advanced NLP techniques with NLTK and SpaCy to extract and preprocess relevant features for forecasting tasks.
5. Optimize model deployment pipelines using Apache Airflow and Hadoop, ensuring efficient workflow orchestration and data management.
6. Collaborate within the development team to troubleshoot, refine, and enhance ML solutions, ensuring adherence to best practices and coding standards.

Skill Requirements

1. Advanced Proficiency In Machine Learning And Natural Language Processing, With Solid Experience In Time Series Analysis And Forecasting.
2. Strong Skills In Python Programming, Including Numpy, Pandas, Scikitlearn, Tensorflow, Pytorch, Xgboost, And Lightgbm.
3. Indepth Knowledge Of Distributed Data Processing With Apache Spark And Realtime Data Integration Using Apache Kafka.
4. Solid Understanding Of Ml Model Evaluation Metrics And Techniques, Including Crossvalidation And Performance Optimization.
5. Experience With Workflow Orchestration Tools Such As Apache Airflow And Big Data Platforms Like Hadoop.
6. Advanced Proficiency In Nlp Libraries Including Nltk And Spacy.

Other Requirements

1. Optional But Valuable:
2. Certifications Such As Tensorflow Developer Certificate
3. - Aws Certified Machine Learning � Specialt