Data Scientist

General Function

The Applied Data Scientist is a business-facing analytics professional responsible for AI research and solution delivery. The role owns the full problem-to-solution journey: engaging stakeholders, translating business challenges into ML solutions, researching emerging AI techniques, and delivering production-ready models that drive measurable business value. It balances innovation with execution, ensuring outcomes are practical, scalable, and aligned to business needs.

Major Duties and Responsibilities

• Engage with stakeholders to understand objectives, constraints, and success metrics
• Deliver end-to-end ML solutions from problem framing to production deployment
• Conduct applied research to evaluate and prototype new AI/ML techniques
• Prioritize between research and delivery based on business impact
• Manage model lifecycle: validation, monitoring, retraining, and optimization
• Translate ambiguous business problems into structured data science statements
• Organize exploratory analysis, experimentation, and research frameworks
• Structure scalable ML pipelines for training, inference, and deployment
• Develop proof-of-concepts to validate new AI techniques
• Convert insights and research outcomes into actionable recommendations
• Drive solutions from research to production
• Act as a trusted analytics advisor through consultative problem-solving
• Promote adoption of ML solutions by communicating value and limitations
• Evaluate and adopt emerging AI/ML techniques
• Mentor junior team members on research and delivery best practices

Basic Qualifications

• Bachelor’s degree in Computer Science, Data Science, Statistics, or related field
• Proven experience delivering ML solutions in production
• Ability to work independently with stakeholders and own delivery
• Strong programming skills in Python or R; experience with ML libraries (scikit-learn, TensorFlow, PyTorch)
• Solid understanding of statistical modeling, ML techniques, and data mining

PREFERRED QUALIFICATIONS

• Master’s or PhD in Data Science, Statistics, or related discipline
• Experience in applied research and rapid prototyping of AI/ML solutions
• Exposure to advanced AI techniques (deep learning, generative AI, optimization)
• Experience deploying ML models using Docker, Kubernetes, or cloud-native services
• Familiarity with Azure, Databricks, Spark, Synapse, and Lakehouse architecture
• Strong storytelling and communication skills for technical and non-technical audiences
• Experience in agile, fast-paced, cross-functional environments
• Balance research depth with delivery focus
• Ability to evaluate and apply new AI techniques pragmatically
• Excellent listening and problem-solving skills
• Strong ownership mindset with minimal supervision
• Ability to translate complex research into business value