Senior Data Scientist (PAI)

We are looking for a Senior Data Scientist to join a team building the foundation for reliable production AI. The role combines strong statistical analysis and applied machine learning with real production awareness. You would work closely with machine learning engineers to ensure models are not only effective in development, but also production-ready, monitored, and continuously improved. The role also includes identifying model-driven opportunities, generating actionable insights, and supporting business and customer recommendations based on real-world performance.

Requirements:

• 5+ years of experience applying machine learning and statistics to business problems;

• Strong proficiency in Python, including libraries such as pandas, scikit-learn, and PyTorch or TensorFlow;

• Strong background in statistical methods, including supervised and unsupervised learning, classification models, experimental design, and data analysis;

• Strong proficiency in SQL, including work with Snowflake, and the ability to collaborate with data engineering teams on raw data and pipeline optimization;

• Practical experience with cloud platforms, especially AWS;

• Solid understanding of APIs, model deployment processes, and monitoring practices;

• Excellent communication skills and the ability to bridge technical and business contexts;

• Strong problem-solving skills and a proactive ownership mindset;

• Comfort working in cross-functional teams with engineers, product managers, and business stakeholders.

Responsibilities:

• Develop, validate, and tune statistical and machine learning models that solve complex business problems;

• Partner closely with engineers to ensure models are designed with production deployment in mind;

• Design experiments and evaluate models using robust statistical methodologies;

• Build and maintain dashboards that monitor product efficacy and distribute key insights across product and customer domains;

• Collaborate on deployment pipelines, APIs, and supporting infrastructure for production ML systems;

• Proactively monitor deployed models for drift, accuracy, and reliability;

• Provide business recommendations and practical insights based on model performance in production;

• Recommend retraining or refinement strategies in response to changing model behavior;

• Translate model results into actionable recommendations for product and business teams;

• Identify opportunities for model improvements that support revenue growth, operational efficiency, and stronger product outcomes;

• Communicate results clearly to both technical and non-technical stakeholders;

• Help evolve data science standards and playbooks with a strong focus on operational impact.

Nice to Have:

• Familiarity with other programming languages such as R, Java, and Go;

• Experience with production ML environments and workflows;

• Experience with telemetry, performance metrics, and GenAI-related systems at scale;

• Experience with data visualization and storytelling in business-facing environments;

• Mentoring experience or the ability to guide teams in applied ML and production-readiness best practices.