Senior Data Scientist - ML/AI Digital Workplace Experience

Key Responsibilities

• Design, build, and deploy production-grade ML models with end-to-end pipelines and MLOps best practices to ensure scalability, reproducibility, and continuous improvement.
• Leverage GenAI solutions leveraging LLMs, RAG, prompt engineering, and fine-tuning to build intelligent assistants, conversational agents, and knowledge retrieval tools.
• Analyze large-scale datasets using statistical methods and advanced analytical frameworks to uncover actionable patterns and measure AI/ML impact.
• Partner cross-functionally to deliver high-impact AI/ML use cases—including predictive analytics, anomaly detection, and workflow automation—across enterprise digital workplace platforms.
• Serve as a subject matter expert in ML and GenAI, mentoring team members, driving actionable business recommendations, and contributing to responsible AI governance frameworks.

Required Qualifications

• 5+ years of hands-on experience delivering production-level ML/AI solutions with strong expertise in Generative AI, LLMs, RAG architectures, and prompt engineering.
• Advanced proficiency in Python, ML/AI frameworks (scikit-learn, TensorFlow, PyTorch, Hugging Face, LangChain), NLP techniques, SQL, and large-scale data platforms (Databricks, Spark, BigQuery).
• Hands-on experience with MLOps practices (model versioning, CI/CD, experiment tracking, monitoring) and cloud platforms (Azure, AWS, GCP) and their AI/ML services.
• Strong foundation in statistics, probability, and experimental design with the ability to apply rigorous analytical methods to real-world problems.
• Excellent storytelling and communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications

• Master's degree or Ph.D. in Data Science, Computer Science, Machine Learning, Statistics, Mathematics, Computational Linguistics, or a related quantitative field.
• Experience with vector databases (Pinecone, Weaviate, FAISS), AI orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel), and enterprise AI platforms such as Azure OpenAI Service or Azure Machine Learning.
• Knowledge of digital workplace technologies (Microsoft 365, ServiceNow, endpoint management) with experience in enterprise telemetry and application usage analytics.
• Familiarity with responsible AI principles and explainability techniques (SHAP, LIME), with experience building real-time inference systems and deploying models via APIs and microservices.
• Experience with computer vision or multi-modal AI, prior work in healthcare or enterprise IT environments, and relevant cloud AI/ML certifications (Azure AI Engineer, AWS ML Specialty, Google Professional ML Engineer).