Senior Data Scientist – ML/AI Digital Workplace Experience

📋 Description

• Design, construct, and implement production-quality ML models utilizing end-to-end pipelines and MLOps best practices to guarantee scalability, reproducibility, and ongoing enhancement.

• Spearhead the creation and deployment of GenAI solutions by leveraging LLMs, RAG, prompt engineering, and fine-tuning to develop intelligent assistants, conversational agents, and knowledge retrieval systems.

• Examine large-scale datasets through statistical techniques and advanced analytical frameworks to identify actionable insights and assess the impact of AI/ML.

• Collaborate across various functions to deliver high-impact AI/ML applications—including predictive analytics, anomaly detection, and workflow automation—within enterprise digital workplace platforms.

• Act as a subject matter expert in ML and GenAI, guiding team members, driving actionable business insights, and contributing to frameworks of responsible AI governance.

⛳️ Requirements

• Over 5 years of practical experience in delivering production-grade ML/AI solutions with a strong focus on Generative AI, LLMs, RAG architectures, and prompt engineering.

• Advanced skills in Python, ML/AI frameworks (such as scikit-learn, TensorFlow, PyTorch, Hugging Face, LangChain), NLP techniques, SQL, and large-scale data platforms (including Databricks, Spark, BigQuery).

• Practical experience with MLOps methodologies (model versioning, CI/CD, experiment tracking, monitoring) and cloud services (Azure, AWS, GCP) along with their AI/ML offerings.

• Solid understanding of statistics, probability, and experimental design with the capability to apply rigorous analytical techniques to real-world scenarios.

• Exceptional storytelling and communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.

🏝️ Benefits

• Medical, dental, and vision coverage

• Paid time off

• Retirement savings options

• Wellness programs

• Additional resources, based on eligibility