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Manager Applied Science information

What is the difference between Manager Applied Science vs Data Scientist?

AspectManager Applied ScienceData Scientist
Required CredentialsAdvanced degrees in science or engineering, leadership experienceDegree in statistics, computer science, or related field
Work EnvironmentLeads teams, manages projects, collaborates with cross-functional teamsAnalyzes data, builds models, provides insights
Employer & Industry UsageTech, manufacturing, biotech, research organizationsTech companies, finance, healthcare, consulting

The main difference is that a Manager Applied Science oversees scientific teams and projects, focusing on leadership and strategic direction, while a Data Scientist primarily analyzes data and develops models. Both roles require strong technical skills, but the Manager Applied Science emphasizes management and coordination within scientific contexts.

What career can I do with a manager applied science?

A Manager in Applied Science typically advances to roles such as senior manager, director, or executive in research, development, or technical operations. They often work in industries like technology, healthcare, or manufacturing, utilizing skills in project management, data analysis, and scientific expertise to lead teams and oversee innovative projects.

What are the most commonly searched types of Applied Science jobs in Arizona?

The most popular types of Applied Science jobs in Arizona are:

What cities in Arizona are hiring for Manager Applied Science jobs?

Cities in Arizona with the most Manager Applied Science job openings:

Manager Applied Science jobs near you

Manager - Applied AI/ML for Hyper-Personalization

Globe Telecom, Inc.

Globe, AZ • On-site

Full-time

Re-posted 11 days ago

Job description

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.

Job Description The manager for Applied AI and Machine Learning for Hyper-Personalization champions the use of AI, data science, machine learning, and advanced analytics in enabling hyper-personalization for campaign management, and drives the day-to-day execution of the data science team, leading the team to develop and implement AI and machine learning models from development to operationalization in the delivery of hyper-personalized campaigns

DUTIES AND RESPONSIBILITIES:

The responsibilities of this position will include but will not be limited to the following:

  • Champion data science, AI, and machine learning to Identify opportunities and partner with stakeholders for the application of AI, machine learning and advanced analytics techniques towards enabling hyper-personalization efforts for customer engagement campaigns, and unlock new AI use cases to drive better take-up or incremental revenues in campaigns.

  • Conceptualize, design, develop and implement machine learning and AI models related to campaign targeting and offers, such as propensity models for predicting different customer states to uncover upsell and churn-save opportunities, as well as recommender systems to improve offer arbitration for customers. - Create models based on the needs and requirements of the campaign squads, and deliver the models end-to-end from exploratory data analysis, to ideation and selection of models, to eventual operationalization and monitoring of model performance.

  • Lead the data science team in the day-to-day execution of the team's tasks in the execution of the team's backlog, and provide guidance on backlog prioritization, and manage team's workload to properly balance delivery, and manage impediments and dependencies raised. Ensure that AI scores and models are running and generated to support the regular campaign runs and delivery of hyper-personalized offers.


Drive cross-functional collaboration for specific campaigns/programs where AI techniques or approaches are involved, providing recommendations on effective and efficient ways to integrate AI and analytics to the work process or platforms involved.

Equal Opportunity Employer
Globe's hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.
Globe's Diversity, Equity and Inclusion Policy Commitment can be accessed here

Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.

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