Senior Data Scientist

Job Responsibilities:

• Research, design, and develop innovative generative AI models and applications.
• Collaborate with cross-functional teams to identify opportunities for AI-driven solutions.
• Train and fine-tune AI models on large datasets to achieve optimal performance.
• Optimize AI models for deployment in production environments.
• Stay up-to-date with the latest advancements in AI and machine learning.
• Collaborate with data scientists and engineers to ensure data quality and accessibility.
• Design, implement, and optimize machine learning algorithms for tasks like classification, prediction, and clustering.
• Develop and maintain robust AI infrastructure.
• Document technical designs, decisions, and processes, and communicate progress and results to stakeholders
• Work with cross-functional teams to integrate AI/ML models into production-level applications

Basic Qualifications:

• Master's degree in a quantitative discipline or equivalent.
• 5+ years minimum professional experience.
• Distinctive problem-solving skills, good at articulating product questions, pulling data from large datasets, and using statistics to arrive at a recommendation.
• Excellent verbal and written communication skills, with the ability to present 
• information and analysis results effectively.
• Ability to build positive relationships within ShyftLabs and with our stakeholders, and work effectively with cross-functional partners in a global company.
• Must have a deep understanding of ML algorithms, ranging from classical methods (e.g., regression, random forests, k-means clustering) to advanced techniques such as gradient boosting (XGBoost, LightGBM, CatBoost), neural networks, and transformer-based architectures (e.g., sentence transformers, BERT variants).
• End-to-End Deployment: Proven experience building, training, and deploying ML models from scratch into production environments, including model lifecycle management (versioning, monitoring, and retraining).
• Scalability & Performance: Hands-on experience operationalizing models at scale, optimizing for performance, reliability, and cost efficiency in real-world production systems.
• Programming: experience with Python or other scripting languages and database language (e.g., SQL) or data manipulation (e.g., Pandas).