Data Scientist II

Key Responsibilities

Technical Excellence (70%)

• Lead full ML lifecycle: problem framing data prep modeling deployment
• Build predictive models (regression, classification, clustering, time-series)
• Apply statistical and advanced analytics to structured & unstructured data
• Develop scalable ML pipelines and deploy models to production
• Design and implement AI agents using LLMs, RAG, memory, tools, and orchestration frameworks
• Evaluate and optimize AI systems using custom metrics and feedback loops
• Stay current with emerging ML and GenAI advancements

Business Partnership (15%)

• Translate business needs into analytical solutions
• Communicate insights clearly to technical and non-technical stakeholders
• Deliver projects on time with defined success criteria

Innovation & Continuous Improvement (10%)

• Improve processes and methodologies
• Develop new analytical capabilities
• Continuously upskill in ML and AI best practices

Administration (5%)

• Complete required trainings and organizational responsibilities

Minimum Qualifications

Education: Masters or Ph.D. in Data Science, Computer Science, Statistics, or related quantitative field
Experience: 5+ years building and deploying ML models

Required Skills

• Strong ML expertise (feature engineering, validation, ensemble models, neural networks)
• Proficiency in Python/R (pandas, NumPy, scikit-learn, etc.)
• Experience with PyTorch, TensorFlow, Keras, or similar
• Hands-on MLOps, Git, and cloud platforms (AWS/GCP/Azure)
• Experience deploying production ML systems
• Strong data storytelling and visualization skills (Shiny, Dash, Tableau)
• Ability to manage multiple projects independently

Preferred Qualification

• 5+ years of experience
• Background in statistics or quantitative sciences
• Certifications in R, Python, or SQL