Senior Lead, Data Science and AI

Kyndryl

. Build innovative internal data science and AI solutions for Kyndryl’s HR organization .

Posted 11/5/2026full-timeRemote • PolandSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates advanced proficiency in Python and SQL, with a strong focus on building and deploying scalable cloud solutions using Microsoft Azure. Expertise in the full machine learning lifecycle and operationalizing AI workloads is essential for success in this role.

Highest-signal resume keywords

Python ProgrammingSQL ProficiencyMicrosoft Azure ServicesMachine Learning LifecycleREST API Development

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data Pipeline DevelopmentMLOps PracticesSoftware Engineering Best PracticesAutomated TestingCI/CD Pipelines

Soft Skills

CollaborationFlexibilityCommunication

Tools & Technologies

Git Version ControlDockerData FabricWorkdayMicrosoft Power Apps

Industry Keywords

AI SolutionsData ScienceHR ReportingCloud SolutionsAgile Team Environment

Tech Stack

Tools & technologies

AzureCloudDockerMicroservicesPythonSQL

About the role

Key responsibilities & impact

Build innovative internal data science and AI solutions for Kyndryl’s HR organization
Develop and maintain an AI agent
Build a natural language data agent on Data Fabric for HR reporting queries
Design, build, and deploy end-to-end full-stack data science and AI solutions
Architect and deploy scalable solutions using Microsoft Azure cloud services
Integrate enterprise data sources such as Workday and Power Apps
Collaborate with distributed teams across the US, Costa Rica, UK, and India
Adjust working schedules to support daily alignment with US East Coast stakeholders
Develop scalable data pipelines, REST APIs, and backend services
Operationalize machine learning and AI workloads
Apply software engineering and MLOps practices to move models from development into production

Requirements

What you’ll need

Advanced proficiency in Python and SQL
Experience designing, building, and deploying scalable cloud solutions using Microsoft Azure services
Hands-on experience building REST APIs, backend microservices, and integrations between enterprise cloud applications such as Workday and Microsoft Power Apps
Deep understanding of the full machine learning lifecycle, including data extraction, model training, deployment, monitoring, and MLOps governance
Software engineering best practices, including Git version control, Docker or other containerization, automated testing, and CI/CD pipelines
Willingness and flexibility to work evenings, typically until 18:00–19:00 CET
Prior professional experience in management or technology consulting (preferred)
Background in a high-growth startup or agile team environment (preferred)
Familiarity with enterprise Data Fabric concepts or human resources data architectures (preferred)

Benefits

Comp & perks

Massive architectural freedom to experiment
Exposure to cutting-edge agentic AI technologies
Dedicated sponsorship to earn certifications across all four major cloud platforms
High visibility across senior leadership
Flexible, supportive environment
Well-being support through Be Well programs covering financial, mental, physical, and social health
Personalized development goals and continuous feedback
Certifications with Microsoft, Google, and Amazon
Coaching and hands-on learning experiences
Flexible, hybrid-friendly culture