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Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformPythonSQL
About the role
Key responsibilities & impact- Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within our AI portfolio.
- Work at the intersection of data science, machine learning, and GenAI, turning complex data into actionable insights, automated decisions, and intelligent workflows.
- Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into real-time or near-real-time decision-making pipelines.
- Collaborate closely with data engineers, ML engineers, analysts, and platform teams, contributing to shared modeling standards and cross-functional AI architecture.
- Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems).
- Apply statistical, predictive, and prescriptive modeling techniques to solve business problems.
- Build reusable modeling frameworks that can scale across multiple domains.
- Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods).
- Translate observational and experimental data into actionable business insights.
- Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs).
- Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows.
- Contribute to the development of intelligent agents and AI-assisted decision-making systems.
- Combine structured data models with unstructured data and GenAI outputs.
Requirements
What you’ll need- Strong experience in machine learning, statistics, and applied data science
- Experience with causal inference, experimentation, or decision science methodologies
- Solid understanding of forecasting, optimization, or analytical modeling techniques
- Strong programming skills in Python and SQL
- Experience building and deploying production-ready data science or ML systems
- Familiarity with model lifecycle management (training, deployment, monitoring)
- Hands-on experience with at least one major cloud platform: Azure (preferred), AWS, or GCP
- Experience working with complex, multi-source datasets (e.g., transactional, behavioral, operational data)
- Ability to translate business problems into analytical frameworks
- Strong problem-solving skills with focus on business impact
- Ability to translate complex models into actionable decisions
- Strong collaboration and communication skills across technical and business teams
Benefits
Comp & perks- Private health insurance
- Education program
- Wellbeing program
- Free beverages
- Events
- Competitive conditions
- Challenging projects
- Cool colleagues
- Honest feedback
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningstatisticsdata sciencecausal inferenceforecastingoptimizationanalytical modelingPythonSQLmodel lifecycle management
Soft Skills
problem-solvingcollaborationcommunicationbusiness impact focusanalytical framework translation