Apply knowledge of advanced analytic algorithms and technologies (e.g., machine learning, deep learning, artificial intelligence) to deliver better predictions and/or intelligent automation that enables smarter business decisions, improved customer experience, and drive productivity.
Use in-depth knowledge of the data science and AI landscape and hands-on expertise to implement solutions in production environments.
Shape advanced conceptual thinking to solve complex or novel situations that have never been dealt with before.
Select and use the appropriate statistical tests and machine learning methods to examine business hypotheses.
Create and mine datasets in Google BigQuery and other company data sources to support analyses.
Work with stakeholders in the organization to identify opportunities for leveraging analysis that drives business solutions.
Explore new technologies to enhance the Data and Analytics team’s data ecosystem.
Aid in the development of best practices used in business metrics analysis.
Support process improvement discussions with stakeholders informed by analytical insights.
Participate in business partner-facing presentations and meetings as needed.
Support a team of analysts in maintaining the data quality and governance of new and existing data sources and pipelines.

Bachelor’s / Master’s degree / Ph.D. in Computer Science, Mathematics, Physics, Engineering, Statistics, or other quantitative disciplines and/or equivalent experience.
Typically, 3+ years of relevant experience and/or certification in a related field of study or an equivalent combination of education and experience.
Experience in data-driven insight development and decision making.
In-depth experience using machine learning algorithms.
Strong coding skills.
Experience with deploying models into production with formal MLOps frameworks.
Experience with distributed computing languages (e.g., Hive / Hadoop/ Spark) & cloud technologies (e.g., AWS Sagemaker, AzureML).
Experience with programming languages (e.g., SQL, Python, R, SAS, SPSS, Perl) and machine learning /deep learning algorithms/packages (e.g., XGBoost, H2O, SparkML, Huggingface).
Collaboration & team skills; with a focus on cross-group collaboration.

Data Scientist

Key skills