Senior Specialist, Data Science & Artificial Intelligence
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Key skills for this role
Role Overview
Apply advanced analytics, machine learning, and Generative AI to improve decision-making, operational performance, automation, innovation, and business outcomes.
Develop scalable, reliable, and responsible AI solutions while embedding governance, security, privacy, and ethical practices.
Key Skills for This Role
Full Job Posting
Why This Role Matters
Apply advanced analytics, machine learning, and Generative AI to improve decision-making, operational performance, automation, innovation, and business outcomes.
Develop scalable, reliable, and responsible AI solutions while embedding governance, security, privacy, and ethical practices.
What You Will Deliver
- Develop and deploy machine learning, deep learning, and Generative AI solutions for strategic and operational challenges.
- Design scalable AI applications and accelerate adoption of advanced analytics and emerging AI technologies.
- Improve model performance through feature engineering, tuning, evaluation, testing, validation, and optimization.
- Build GenAI applications using prompt engineering, retrieval-augmented generation, and large language models.
- Prepare structured and unstructured data for AI use cases through cleansing, preparation, and feature development.
- Support AI deployment, monitoring, retraining, and lifecycle management using MLOps and LLMOps.
- Ensure compliance with governance, cybersecurity, privacy, and ethical AI requirements.
Minimum Qualifications and Experience
- Bachelor’s degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative discipline.
- 4–6 years of experience in data science, machine learning, artificial intelligence, or advanced analytics.
- Experience developing and deploying AI solutions in business environments.
- Experience with structured and unstructured data for analytical and AI use cases.
- Experience supporting AI deployment, monitoring, and model lifecycle management.
Skills That Matter
- Machine learning, deep learning, Generative AI, large language models, prompt engineering, and retrieval-augmented generation.
- Model fine-tuning, evaluation, data preparation, feature engineering, model monitoring, and performance optimization.
- Python, SQL, TensorFlow, PyTorch, Scikit-learn, API integration, AI deployment, MLOps, and LLMOps.
- Analytical problem solving, collaboration, stakeholder engagement, communication, accountability, and continuous improvement.
Success Measures
AI solutions deliver measurable business value and operational improvement.
Generative AI applications provide accurate, reliable, high-quality outputs and integrate successfully into enterprise processes.
Models remain effective throughout their lifecycle, while MLOps, LLMOps, governance, security, and responsible AI practices are consistently applied.
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