This night-shift post on the cardiac ward pairs patient care with mentoring new nurses. This role will operate as a versatile technical problem-solver, developing solutions for a wide range of business challenges rather than supporting one specific product or functional area. The ideal candidate combines strong traditional machine learning and data engineering fundamentals with practical experience building Generative AI applications, leveraging agentic coding tools, and deploying production-ready solutions. This is an extremely hands-on engineering role, with approximately 95% of the work focused on coding, building, modeling, engineering, and deploying solutions. The remaining time involves internal collaboration, technical discussions, and occasional stakeholder interaction. Candidates must be comfortable taking ambiguous business problems, identifying appropriate technical approaches, and independently developing working solutions.
Responsibilities
Develop, optimize, and maintain complex SQL queries involving joins, CTEs, window functions, and query performance tuning
Build and manage data pipelines using Python, PySpark, and related data engineering technologies
Extract, transform, and integrate data from multiple enterprise sources to support analytics and machine learning initiatives
Design scalable data processing workflows and ensure data quality, accuracy, and performance
Work independently to troubleshoot data engineering challenges and develop efficient technical solutions
Design, develop, and deploy traditional machine learning models supporting forecasting, classification, regression, and predictive analytics
Build models for business use cases such as demand forecasting, lead scoring, customer propensity, and other predictive applications
Apply machine learning frameworks including XGBoost and LightGBM to develop production-ready models
Utilize SHAP and other explainability techniques to interpret model predictions, debug model behavior, and communicate findings to nontechnical stakeholders
Evaluate model performance, optimize predictive accuracy, and identify opportunities for continuous improvement
Translate business requirements into practical machine learning solutions with measurable outcomes
Design, build, and deploy Generative AI applications using modern LLM technologies
Develop Retrieval-Augmented Generation (RAG) solutions, vector search capabilities, and AI agents with tool-use functionality
Integrate LLM APIs into applications, automation workflows, and enterprise solutions
Build scalable asynchronous or batch-processing workflows capable of handling thousands of LLM API calls
Apply LLMs to practical use cases such as data labeling, metadata extraction, text processing, and workflow automation
Millions of jobs, with real people getting hired every day
200,000+
New jobs added daily
7,000,000+
Verified job listings
1,000,000+
Tailored applications submitted
Leverage agentic coding platforms such as Claude Code, OpenAI Codex, Cursor, or comparable tools as part of daily software development
Use AI-assisted development workflows to accelerate coding, debugging, testing, and solution delivery
Develop and deploy machine learning, data science, and AI applications using cloud-based platforms
Work within Microsoft Azure, Databricks, or comparable cloud environments, including AWS and Google Cloud
Build and support containerized applications and cloud-based services
Move solutions beyond notebooks and prototypes into functional applications, APIs, or production environments
Collaborate with technical teams to ensure solutions are scalable, reliable, and maintainable
Support end-to-end technical implementation, troubleshooting, and ongoing optimization
Develop proofs of concept and working prototypes to validate new business use cases
Translate loosely defined business problems into executable technical solutions
Work across multiple concurrent initiatives involving data engineering, predictive modeling, AI, and automation
Evaluate technical approaches and select appropriate tools, frameworks, and architectures
Communicate technical questions, blockers, and recommendations clearly to team leadership
Independently manage technical deliverables in a fast-moving environment with limited oversight
Requirements
4+ years of strong SQL experience, including complex joins, CTEs, window functions, query optimization, and performance tuning
4+ years of hands-on Python development experience
2+ years of experience with PySpark or comparable distributed data processing technologies
Strong experience building data pipelines, manipulating large datasets, and integrating multiple data sources
Demonstrated hands-on experience developing and deploying forecasting and classification models for real-world business applications
Experience with predictive modeling use cases such as demand forecasting, lead scoring, customer propensity, or similar applications
Production experience with XGBoost and/or LightGBM
Hands-on experience using SHAP to explain, interpret, and troubleshoot machine learning models
Strong understanding of regression, classification, forecasting, feature engineering, model evaluation, and predictive analytics
Machine learning experience must come from production environments or substantial real-world projects; coursework alone is insufficient
6+ months of daily, hands-on agentic coding experience using Claude Code, Codex, Cursor, or comparable development agents
Demonstrated experience personally building Generative AI products or applications, including: Retrieval-Augmented Generation (RAG), Vector search, AI agents with tool-use capabilities
Experience integrating LLM APIs into scalable applications or automated workflows
Experience executing large-scale LLM API workloads, including asynchronous or batch-processing jobs involving thousands of API calls
Must be an active technical builder with practical software development experience, rather than someone whose background is limited to AI strategy, advisory work, or prompt experimentation
Hands-on experience with Azure and/or Databricks, with experience in both strongly preferred
Strong experience with Google Cloud / Vertex AI or AWS / SageMaker is also acceptable
Ability to develop, package, and deploy working machine learning, data engineering, or AI solutions
Familiarity with cloud infrastructure, application deployment, and production environments
Nice to have:
Experience developing recommendation engines or personalization systems
Experience building web applications using agentic coding tools
Experience developing APIs with FastAPI or comparable Python frameworks
Experience with Docker, containerized applications, and end-to-end deployment
Familiarity with Kubernetes, Azure Kubernetes Service (AKS), or comparable orchestration platforms
Front-end development experience with React and/or Next.js
Experience developing production applications that combine data engineering, machine learning, Generative AI, and user-facing interfaces
Experience supporting multiple business functions and rapidly adapting to unfamiliar technical challenges
Important Details
Contract: 6 months initially, potential for extension
Start date: ASAP
About the Company
Our client is seeking a highly technical, hands-on Senior Data Scientist / Applied AI Engineer to support a variety of machine learning, data engineering, predictive analytics, and Generative AI initiatives within a large enterprise environment.
