Senior Data Scientist (Data Cloud Acceleration)

Join our team as a Senior Data Scientist, where you will independently own defined deliverables, build models and analyses, and support intelligence products. You will work across various revenue and intelligence initiatives, collaborating with application engineers, analysts, and business stakeholders. This hands-on role requires strong Python skills, experience with cloud data platforms, and the ability to explain analytical results to both technical and non-technical stakeholders.

• Independently own defined deliverables, from understanding the requirement and preparing the data through modeling, validation, documentation, and delivery.
• Translate business questions into analytical approaches, ask clarifying questions, understand how the output will be used, and recommend an approach that fits the decision, timeline, and available data.
• Build and test models quickly, develop practical solutions using statistical methods, machine learning, deep learning, or existing models and services where appropriate.
• Familiarity with orchestration and automation tools such as Airflow, AWS Glue, Prefect, or similar platforms
• Meaningful use of GenAI tools to improve the speed and quality of day-to-day work
• Strong Python skills and familiarity with libraries such as pandas, scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow
• Experience with cloud data platforms such as Snowflake, Databricks, Athena, Hive, BigQuery, or similar technologies
• Experience using version control, testing, and reproducible development practices
• Experience developing classification, regression, clustering, forecasting, recommendation, optimization, or anomaly-detection solutions
• Ability to explain analytical results and trade-offs to technical and nontechnical stakeholders
• Experience creating repeatable batch-scoring workflows or exposing model outputs through APIs or services
• Strong SQL skills and experience analyzing large datasets
• Experience applying statistical analysis and machine learning to real business problems
• Familiarity with model evaluation, experimental design, feature engineering, and statistical validation
• Work well with ambiguity. You can turn an incomplete request into a clear set of questions, assumptions, and next steps
• Move quickly with discipline. You can produce an initial version rapidly while still validating the fundamentals
• Think beyond the notebook. You consider how a model will be refreshed, accessed, demonstrated, monitored, and reused
• Understand the business context. You evaluate technical decisions through the lens of client outcomes, revenue, cost, adoption, and decision quality
• Are curious and low-ego. You are comfortable learning from others, revising an approach, and using an existing solution when it is better than building a new one
• Are pragmatic. You select the simplest credible approach that can deliver useful business impact
• Care about trust. You check the data, question surprising results, and make limitations visible