Data Scientist (AI Customer Support and Safety)

About the role

The AI Solution Delivery team is expanding its NLP capabilities to create production‑grade AI products that automate customer support operations. As a Data Scientist you will design, build, and deploy sophisticated models that ensure reliable, scalable support solutions.

Key responsibilities

• Lead the full machine‑learning lifecycle from research and hypothesis testing to production deployment and ongoing maintenance.
• Translate business objectives into well‑defined data‑science problems with clear, quantifiable metrics.
• Design and implement robust, scalable ML systems, including data analysis, annotation, and processing pipelines.
• Integrate models with existing backend services and contribute to overall system architecture.
• Monitor deployed models, detect concept drift, and proactively address performance issues.
• Mentor junior team members and support onboarding initiatives.
• Automate repetitive tasks and propose innovative solutions that drive business impact.
• Communicate technical concepts clearly to both technical and non‑technical stakeholders.

Required profile

• Minimum 5 years of professional experience in data science or machine learning with a strong focus on NLP.
• Academic background in a quantitative discipline such as Computer Science, Mathematics, or related field (preferred).
• Proven ability to design, test, and deploy ML models in production environments.
• Experience with experimental design and measuring solution effectiveness.
• Understanding of security, risk, and control considerations in production systems.

Required skills

• Python
• Pandas, NumPy, Scikit‑learn
• Classic machine‑learning and deep‑learning techniques
• MLOps and production ML system design
• Streaming, batch, and asynchronous data processing
• Software system design principles
• Event‑driven deployment environments

What we offer

• Opportunity to impact millions of users across 1,100+ cities in 48 countries.
• Access to mentoring, career consulting, and continuous learning programs.
• Collaboration with international teams and exposure to global AI challenges.