Data Scientist

Overview

In this Data Scientist role, you will collaborate with a global, world-class team to apply advanced machine learning and statistical analysis to build models that drive customer experience, demand forecasting, operations optimization, and revenue growth. You will leverage Python and modern ML frameworks, from classical algorithms to deep learning architectures, to design, train, and deploy models that move seamlessly from research into production at scale.

While you will use Snowflake and SQL for data access and preparation, your primary focus will be on developing robust, scalable, and innovative models that deliver measurable business impact. You will be expected to contribute across the entire model lifecycle—from exploratory analysis and feature engineering in notebooks through deployment and monitoring to continuous refinement and improvement—working closely with teammates who provide engineering, data, and infrastructure support.

This role requires close collaboration with cross-functional partners such as business leaders, analysts, product managers, and engineers to identify high-value opportunities for AI / ML integration. You will help translate complex analytical insights into actionable strategies, with a strong emphasis on innovation, critical thinking, and advanced modeling approaches that align with business objectives and drive meaningful organizational outcomes.

Responsibilities

• Develop and deploy predictive models to support customer experience, demand forecasting, operations optimization, customer behavior analysis, and revenue trends in B2B / DX environments.
• Design and implement production-grade machine learning models using both classical algorithms (SVM, Gradient Boosting, Random Forests, etc.) and modern deep learning (LSTM, Transformers).
• Build, monitor, and optimize recommendation systems that enhance personalization and customer engagement.
• Apply clustering, data modeling, and forecasting techniques to uncover insights from structured and unstructured datasets.
• Transition models from exploratory notebooks to production-ready artifacts (e.g., serialized pickles or containers) with proper engineering practices.
• Ensure smooth end-to-end MLOps integration, including CI / CD pipelines, model versioning, deployment, monitoring, and retraining.
• Perform advanced statistical analysis on business metrics to deliver specialized reporting and actionable insights.
• Contribute to experimentation and model evaluation through A / B testing, KPI definition, and impact measurement.
• Collaborate with cross-functional teams to identify opportunities for AI / ML integration into business processes and products.
• Communicate results, assumptions, model limitations, and business recommendations to non-technical and technical stakeholders.
• Share knowledge with peers, explain modeling approaches, and foster a data-driven culture across teams. Promote and advocate for data science best practices within the organization.

Qualifications

We value

Whats in for you

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