Change the world. Love your job.
We are seeking a talented and innovative Data Scientist to join our Digital Experience Analytics team to drive our personalization and customer journey modeling efforts. You'll work in a dynamic environment, leveraging advanced data science techniques to enhance TI's sales and marketing initiatives across TI.com and other digital channels. The ideal candidate will have a strong background in data science, machine learning, and marketing analytics, with experience in developing predictive models and deriving actionable insights from large datasets. You will collaborate closely with our marketing, sales, and IT teams to deliver data-driven solutions that support key business objectives.
Key Responsibilities:
Minimum requirements:
Experience deploying models in production and adjusting models to improve performance.
Strong proficiency in Python or R for data analysis and machine learning.
Expertise in statistical analysis, experimental design, and causal inference.
Preferred qualifications:
Strong proficiency in SQL and experience working with large datasets.
Familiarity with both cloud-based data science platforms (e.g., Google Cloud AI, AWS SageMaker), and unix-based systems.
Innovator level knowledge of developments in data science and adjacent fields (ex. Generative AI) and their application.
Strong problem-solving skills and ability to translate complex analyses into actionable insights.
Excellent communication skills, with the ability to present technical concepts to non-technical audiences.
Experience with marketing automation and CRM systems (e.g., Salesforce, Marketo, HubSpot).
Experience with Customer Data Platforms (e.g., Tealium, Segment, ActionIQ).
Knowledge of client-side web analytics tools and techniques (e.q., GA4, Adobe Analytics, ContentSquare, Fulltory).
Familiarity with data visualization tools (e.g., Tableau, Looker, Power BI).
Experience with natural language processing (NLP) and text analytics.
Understanding of digital marketing concepts and customer journey mapping.
Experience with version control systems (e.g., Git) and collaborative data science workflows.