AI Data Scientist

Who We Are
Join our global Centre of Excellence as a subject matter expert (SME) for Agentic AI Services, collaborating with teams worldwide to drive innovation and excellence. As part of this central function, you will help shape, implement, and scale cutting-edge Agentic AI solutions across the organization and our clients

This hands-on role requires deep expertise in artificial intelligence, machine learning, and data to design, build, and deploy advanced Agentic AI-driven solutions tailored to clients' unique needs.

The Data Scientist is a deeply skilled subject matter expert tasked with leading organizational adoption of modern data science, machine learning, and advanced analytics solutions. The primary goal of this role is to design, build, and launch scalable data-driven models and tools that deliver actionable business value, utilizing statistical modeling, machine learning algorithms, data mining, and data visualization.

The role places a priority on leveraging Microsoft's data and AI platforms (such as Microsoft Fabric, Azure ML, and Power BI) to prepare, operationalize, and automate AI services with agentic capabilities. The Data Scientist also plays a key role mentoring less experienced team members and fostering a robust data science culture.

What You'll Be Doing

• Leads the research, design, development, and deployment of end-to-end data science solutions, including advanced modeling and machine learning pipelines, leveraging Microsoft Fabric, Azure Machine Learning, Synapse, and Python/R.
• Partners with business and technology stakeholders to identify, frame, and prioritize AI opportunities that will drive maximum business value and operationalize Agentic AI use cases.
• Collaborates cross-functionally to consolidate, process, and transform structured and unstructured data from multiple sources (including cloud and on-premises), ensuring robust feature engineering, data integrity, and AI-readiness.
• Designs and enhances data collection and labeling frameworks to optimize datasets for predictive modeling, natural language processing, and generative AI tasks within the Microsoft ecosystem.
• Develops, validates, and tunes statistical models, machine learning algorithms, and agentic architectures to solve business, operational, and customer-centric problems.
• Builds, automates, and maintains reusable code, software programs, Azure ML pipelines, and processes for efficient data cleansing, integration, and scalable inferencing.
• Analyzes, interprets, and clearly communicates findings, generating powerful insights and recommendations for business and technical stakeholders, using Power BI and other Microsoft visualization tools.
• Drives adoption of industry best practices and standards in code quality, reproducibility, and responsible AI/MLOps in a production environment leveraging Microsoft Fabric and Azure ML.
• Contributes to AI model monitoring and continuous improvement by collecting feedback, enabling retraining, and adapting solutions based on real-world performance.
• Collaborates closely with Data Engineers and Architects to ensure a seamless data pipeline from source through to model operationalization, agentic orchestration, and business consumption.
• Documents processes, maintains version control, and ensures traceability and compliance across model and data workflows.
• Coaches and mentors less-experienced data scientists, supporting professional growth and fostering innovative thinking across the Agentic AI Services team.

What You'll Bring Along

• Bachelor's degree or higher in Data Science, Computer Science, Mathematics, Engineering, Statistics, Business Analytics, or a related quantitative field.
• Minimum 5-10 years of experience delivering data science solutions in a corporate or relevant industry, including statistical modeling, machine learning, and AI applications.
• Relevant Microsoft certifications (e.g., Azure Data Scientist Associate, Microsoft Certified: Fabric Analytics Engineer), or data science programming certifications preferred.
• Deep expertise in statistical analysis, data modeling, and machine learning (supervised, unsupervised, natural language processing, generative AI).
• Advanced skills in Python (preferred), R, and expertise leveraging Microsoft Azure Machine Learning, Synapse Analytics, and Microsoft Fabric.
• Strong familiarity with cloud-based data science solutions, MLOps, scalable model deployment, and operationalization of agentic AI applications.
• Proficient in extracting, processing, and cleaning large and complex data sets from diverse sources, ensuring quality and AI-readiness.
• Demonstrated ability to translate technical results into impactful business recommendations and clear visualizations (using Power BI and other tools).
• Deep understanding of data privacy, security, and responsible AI principles.
• Ability to tell a compelling story with data and communicate complex findings effectively to both technical and non-technical audiences.
• Self-motivated, strategic thinker, with a curiosity and passion for applying data science for competitive business advantage.
• Strong mentoring, coaching, and collaboration skills; experience fostering a growth-oriented, innovative data science team culture.
• Awareness of the latest trends and advancements in Agentic AI, Microsoft AI ecosystem, and broader data science discipline.
• Demonstrated experience deploying models using Microsoft Azure ML, Synapse, or other cloud-based solutions.
• Strong hands-on experience with programming languages such as Python and R, and SQL for data extraction and processing.
• Significant experience working end-to-end in the data science lifecycle: data extraction, engineering, modeling, operationalization, and monitoring.
• Proven track record visualizing and communicating data insights using Power BI or similar Microsoft tools.
• Proven ability to collaborate with data engineers and architects to create and operationalize AI-ready data environments for Agentic AI.
• Experience in mentoring or coaching junior data scientists.
• Excellent command of both spoken and written English.