Job Information
Insight Global Data Scientist - ML Operations in Miami, Florida
Job Description
Our client in the cruiseline industry is looking for a Data Scientist to join their expanding data-science practice. As a Data Scientist with an ML Ops focus, you will design predictive and prescriptive models (e.g., personalization, CLV, MMM) and build the automated pipelines that keep those models fresh and reliable in production. You’ll work together with Marketing, Sales, Revenue Management, and Data Engineering teams to turn experimentation into always-on intelligence that drives acquisition, personalization, and revenue growth.
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
• 2+ years of experience within data science/machine learning
• Experience in Python and relevant ML libraries (Statistical testing, time-series & casual impact methods, marketing KPIs)
• Experience with large scale data engineering (SQL) in cloud environments (Snowflake or Databricks)
• Understanding of version control and CI/CD tools (GitHub Actions, Model Registry), ML lifecycle best practices including testing, validation, deployment, monitoring, and governance • Experience in marketing or sales analytics