Sr Manager, Machine Learning

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Job Overview
The Sr Manager, Machine Learning at T-Mobile is instrumental in leading the strategic implementation and operational management of AI and ML projects. This role is pivotal in coordinating multi-functional teams to optimize processes and integrate AI/ML models seamlessly into business operations, enhancing operational efficiency and innovation. With a strong focus on leadership, the Sr Manager is responsible for critical decisions related to team development, resource planning, and budget allocation. By collaborating with various teams, this position ensures the alignment of AI and ML operations with organizational goals, driving significant advancements in T-Mobile's technological capabilities. Ultimately, the Sr Manager, Machine Learning contributes to the company's competitive edge and long-term success through strategic oversight and effective management of AI and ML initiatives.

We pride ourselves on encouraging a culture of innovation, advocating for agile methodologies, and promoting visibility in all that we do. Join us in embodying the spirit of the 'Un-carrier' and make a tangible impact! Our team is dynamic where no day is the same, and we are diverse and inclusive passionate about growth and transformation. If you're up to the challenge, apply today!

Job Responsibilities:

• Leads the strategic implementation and operational management of AI and ML projects, ensuring alignment with organizational goals
• Coordinates multi-functional teams to integrate AI/ML models into business operations, enhancing efficiency and innovation
• Manages critical decisions related to team development, resource planning, and budget allocation
• Collaborates with various departments to ensure seamless operation and implementation of AI and ML technologies
• Drives advancements in technological capabilities through effective leadership and strategic oversight
• Evaluates project outcomes and adjusts strategies to meet business needs and technological trends

Education and Work Experience:

• Bachelor's Degree Computer Science, Data Science, or Related Field (Required)
• Master's/Advanced Degree Computer Science or Data Science (Preferred)
• Experience leading and managing a team in the field of machine learning or data science (Preferred)
• Experience with data querying, wrangling, cleaning, and feature engineering in big data environments such as AWS, Azure, and Google Cloud (Preferred)
• Experience building and deploying data mining, visualization, statistical modeling, and machine learning solutions using various supervised/unsupervised ML algorithms (Preferred)

Knowledge, Skills and Abilities:

• Leadership Strong leadership skills with the ability to lead and inspire cross-functional teams. (Required)
• Strategy Exceptional critical thinking and problem-solving abilities, with a deep understanding of AI/ML technologies and applications. (Required)
• Collaboration Excellent communication, negotiation, and interpersonal skills to collaborate effectively with technical and non-technical stakeholders. (Required)
• Project Management Strong project management skills, with the ability to manage complex AI/ML initiatives and prioritize competing demands. (Required)
• Innovation Results-oriented outlook with a demonstrated ability to achieve AI/ML-related goals and drive innovation. (Required)
• Engineering In-depth knowledge and experience in engineering and artificial intelligence/machine learning, critical for the technical oversight and strategic implementation of AI/ML solutions. (Required)

Licenses and Certifications:

• Certified Machine Learning Professional (CMLP): Certification that validates expertise in machine learning concepts and the ability to apply them to real-world problems. (Preferred)
• Project Management Professional (PMP) Internationally recognized certification that demonstrates proficiency in project management and the ability to lead and direct projects. (Preferred)
• Certified Data Scientist (CDS): Certification that endorses skills in data science methodologies and tools, relevant for managing and analyzing large datasets. (Preferred)

• At least 18 years of age
• Legally authorized to work in the United States