Is Best Described As Dynamic And Ambitious
Banking
DataScientist
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Scientist at Is Best Described As Dynamic And Ambitious. Skills: Data Science, AI, MLOps. Develop statistical models. Deploy statistical models”
What You'll Achieve.
Unlock value through AI
Industry & Context.
Translate business problems
What They're Looking For.
Must Have
Bachelor's or Master's degree, 5+ years of experience, Python proficiency, SQL proficiency
Nice to Have
PhD preferred, GCP Professional Data Engineer certification, AWS Data Analytics certification, Databricks Certified certification, Dbt Certified certification
What You'll Do.
Develop statistical models
Deploy statistical models
Maintain statistical models
Maintain AI solutions
Build solutions on Databricks
Maintain solutions on Databricks
Implement MLOps best practices
Integrate models into data platform
Build intelligent AI implementations
Ensure production-grade code
Maintain model documentation
Maintain pipeline documentation
Maintain process documentation
Collaborate with stakeholders
Contribute best practices
Align solutions with architecture
Align solutions with governance
How You'll Work.
Team & Collaboration
Cross-functional teams; Data engineers; Business stakeholders
Communication Scope
Technical communication; Non-technical communication
Full Job Description
Summary: Join Data & Analytics as Data Scientist and help build the next generation of data and AI-driven solutions. Job Description: _**What will you do?**_ This role sits at the intersection of business, data engineering, and AI innovation, where you will translate business problems into scalable data and AI use cases. You will play a key role in designing, developing, deploying, and maintaining statistical (credit risk) models and AI solutions, with a strong focus on Databricks and modern cloud platforms (Azure). Beyond traditional data science, you will actively contribute to AI solution design, leveraging tools such as Azure AI Foundry, LangChain, and LangGraph, enabling the business to unlock value through cutting-edge AI capabilities. **Responsibilities** 1\. Data Science & Modelling * Develop, deploy, and maintain statistical and machine learning models; * Ensure models are production-ready, explainable, and compliant; * Apply best practices in experimentation, validation, and testing; * Build and maintain solutions on Databricks, including pipelines and model deployment workflows; * Implement MLOps best practices to ensure scalability, reliability, and monitoring; * Collaborate with engineers to integrate models into the enterprise data platform. 2\. AI & Advanced Analytics * Design and develop AI solutions using frameworks such as LangChain and LangGraph; * Work with Azure AI Foundry to build and operationalize AI use cases; * Translate business problems into technical AI solutions (know where to use AI and where not to); * Enable business teams with intelligent, scalable, and practical AI implementations. 3\. Quality & Engineering Excellence * Treat testing as an integral part of development, including unit, integration, and data validation tests; * Ensure high-quality, production-grade code and solutions; * Maintain robust documentation for models, pipelines, and processes. 4\. Collaboration & Architecture * Work closely with business stakeholders, data
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