Job Description

Full job description

Be unstoppable with us!
T-Mobile is synonymous with innovation–and you could be part of the team that disrupted an entire industry! We reinvented customer service, brought real 5G to the nation, and now we’re shaping the future of technology in wireless and beyond. Our work is as exciting as it is rewarding, so consider the career opportunity below as your invitation to grow with us, make big things happen with us, above all, #BEYOU with us. Together, we won’t stop!

Job Overview
The Principal Data Scientist is responsible for leading technical innovation within the data science team. The Principal Data Scientist collaborates with a multi-disciplinary team of technical and non-technical business stakeholders on a wide range of challenges and develops the next generation of advanced analytics and Machine Learning (ML) modeling products and solutions. The Principal Data Scientist is entrusted with developing the Artificial Intelligence (AI) that drives the most important aspects of the business. This position is at the forefront of new AI technologies and represents mastery of existing machine learning techniques. Paramount to this job is the understanding that a Principal Data Scientist is a forward-thinking individual contributor that helps define the long-term technical vision of the data science teams within T-Mobile.

Job Responsibilities :

• Extract, prepare, and model large, complex data sets using a combination of skills, including machine learning theory, mathematics, statistics, and programming.

• Identify critical and emerging technologies that will support and extend our consumer data, data integration, and quantitative analytic capabilities.

• Lead the implementation, assessment and standardization of advanced analytics and modeling toolkits for our data science teams.

• Partner with data engineering teams to shape data management strategies, architecture, governance, pipelines, and infrastructure enabling effective modeling environments for data science teams.

• Apply language models (NLP/LLM) to provide customer contact insight and answer business questions.

• Provide senior level guidance to the data science and measurement science teams on approach and methodologies.

• Communicate important information and insights to business leaders using verbal, written, and data visualization skills.

Education :

• Bachelor's Degree Quantitative Subject area (math, statistics, economics, computer science, physics, engineering) (Required)

• Master's/Advanced Degree Quantitative Subject area (math, statistics, economics, computer science, physics, engineering) (Preferred)

Work Experience :

• 7-10 years Industry experience in predictive modeling, data science, and analysis in an ML engineer or data scientist role building and deploying ML models or hands on experience developing deep learning models (Required)

• 4-7 years Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations (Required)

• 7-10 years Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data (Required)

• 4-7 years Experience with statistical methods and advanced modeling techniques. For example- SVM, Random Forest, graph models, Bayesian inference, neural networks, NLP, LLM (Required)

• 4-7 years Experience with big data architecture and pipeline, Hadoop, Hive, Spark, etc. (Required)

• 7-10 years Experience with data scripting languages (e.g., SQL, Python, R) (Required)

• 4-7 years Experience in data visualization (Required)

• 7-10 years Extended experience working with relational database using SQL (Required)

• 1-3 years Experience in implementing and deploying LLMs in a production environment (Preferred)

• 4-7 years Experience in telecom industry (Preferred)

Knowledge, Skills and Abilities :

• Mathematics Calculus, linear algebra, statistics, and probability (Required)

• Programming Mastery of Python and SQL (Required)

• Machine Learning: Apply machine learning techniques including supervised and unsupervised learning; collaborative filtering; and artificial neural networks (Required)

• Deep Learning: Research, design, implement, evaluate, and optimize novel algorithms/models in Deep Learning space (Required)

• LLM prompt engineering and techniques

• Communication Exemplary communication skills, ability to work with cross functional teams (Required)

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

Travel :
Travel Required (Yes/No):No



DOT Regulated :
DOT Regulated Position (Yes/No):No
Safety Sensitive Position (Yes/No):No

Washington Pay Range : $165,600.00 - $224,000.00

The pay range above is the general base pay range for a successful candidate in the state listed. The successful candidate’s actual pay will be based on various factors, such as work location, qualifications, and experience, so the actual starting pay may be above or below this range. At T-Mobile, employees in regular, non-temporary roles are eligible for an annual bonus or periodic sales incentive or bonus, based on their role. Most Corporate employees are eligible for a year-end bonus based on company and/or individual performance and which is set at a percentage of the employee’s eligible earnings in the prior year. Certain positions in Customer Care are eligible for monthly bonuses based on individual and/or team performance, while Retail and Business Sales roles are eligible for monthly or quarterly sales incentives. And since we are ALL owners, EVERY employee at T-Mobile is eligible for an Annual Stock Grant.

At T-Mobile, our benefits exemplify the spirit of One Team, Together! A big part of how we care for one another is working to ensure our benefits evolve to meet the needs of our team members. Full and part-time employees have access to the same benefits when eligible. We cover all of the bases, offering medical, dental and vision insurance, a flexible spending account, 401(k), employee stock grants, employee stock purchase plan, paid time off and up to paid 12 holidays - which total about 4 weeks for new full-time employees and about 2.5 weeks for new part-time employees annually - paid parental and family leave, family building benefits, back-up care, enhanced family support, childcare subsidy, tuition assistance, college coaching, short and long term disability, voluntary AD&D coverage, voluntary accident coverage, voluntary life insurance, voluntary disability insurance, and voluntary long-term care insurance.

We don't stop there- eligible employees can receive mobile service & home internet discounts, pet insurance, and access to commuter and transit programs! To learn about T-Mobile’s amazing benefits, check out

Never stop growing!
T-Mobile doesn’t have a corporate ladder–it’s more like a jungle gym of possibilities! We love helping our employees grow in their careers, because it’s that shared drive to aim high that drives our business and our culture forward.


T-Mobile USA, Inc. is an Equal Opportunity Employer. All decisions concerning the employment relationship will be made without regard to age, race, ethnicity, color, religion, creed, sex, sexual orientation, gender identity or expression, national origin, religious affiliation, marital status, citizenship status, veteran status, the presence of any physical or mental disability, or any other status or characteristic protected by federal, state, or local law. Discrimination, retaliation or harassment based upon any of these factors is wholly inconsistent with how we do business and will not be tolerated.


Talent comes in all forms at the Un-carrier. If you are an individual with a disability and need reasonable accommodation at any point in the application or interview process, please let us know by emailing ApplicantAccommodation@t-mobile.com or calling 1-844-873-9500. Please note, this contact channel is not a means to apply for or inquire about a position and we are unable to respond to non-accommodation related requests.

Job Tags

Holiday work, Full time, Temporary work, Part time, Work experience placement, Local area, Flexible hours,

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