Interested in this Data Scientist role at T-Mobile?
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About This Role
At T-Mobile, we invest in YOU! Our Total Rewards Package ensures that employees get the same big love we give our customers. All team members receive a competitive base salary and compensation package - this is Total Rewards. Employees enjoy multiple wealth-building opportunities through our annual stock grant, employee stock purchase plan, 401(k), and access to free, year-round money coaches. That’s how we’re UNSTOPPABLE for our employees!
This is not a remote position. T-Mobile is a hybrid work environment requiring work in the office three (3) days per week. The successful candidate will be located in either Seattle, Washington or Overland Park, Kansas areas.
Job Overview
This role drives technical innovation across the data science function, applying advanced analytics, machine learning, and artificial intelligence to solve complex business challenges. It partners with technical and non-technical stakeholders to develop modeling solutions that support critical operations. Success is measured by effective implementation, adoption of emerging technologies, and delivery of actionable insights that strengthen data-driven decision-making and advance team capabilities.Job Responsibilities:
- Extract, prepare, and model large, complex data sets to develop advanced analytics and machine learning solutions
- Identify and evaluate new technologies to enhance data integration and quantitative analytics capabilities
- Own the implementation and standardization of advanced analytics and modeling toolkits for data science teams
- Collaborate with data engineering to shape data management strategies, architecture, governance, and infrastructure
- Provide senior-level guidance on data science methodologies and approaches to technical teams
- Communicate insights and technical information to business leaders through verbal, written, and visual means
Also responsible for other duties/projects as assigned by business management as needed *
Required Education:
Bachelor's Degree plus 7 years of related work experience OR Advanced degree with 5 years of related experience . Acceptable areas of study include Quantitative Discipline (math, statistics, economics, computer science, physics, engineering)
*
Required 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
- 4-7 years Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations
- 7-10 years Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data
- 4-7 years Experience with statistical methods and advanced modeling techniques. For example- SVM, Random Forest, graph models, Bayesian inference, NLP, Computer Vision, neural networks
- 4-7 years Experience with big data architecture and pipeline, Hadoop, Hive, Spark, Kafka, etc.
- 7-10 years Experience with data scripting languages (e.g., SQL, Python)
- 4-7 years Experience in data visualization
7-10 years Extended experience working with relational database using SQL *
Preferred Experience
- 4-7 years Experience in telecom industry
Required Knowledge, Skills and Abilities:
- Advanced Analytics
- Critical Thinking
- Data Science
- Data Visualization
- Generative AI
- Large Language Model (LLM)
- Machine Learning (ML)
Statistical Analysis *
Preferred Knowledge, Skills and Abilities:
- Data Bricks
- Deep Learning
- XGBoost
- LGBM
- PySpark
- Reinforced Learning
#LI-Corporate
- 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
Total Target Cash Earnings Opportunity: $152,880 - $275,760 National Base Pay Range: $127,400 - $229,800 The Total Target Cash Earnings Opportunity represents the expected total cash compensation for this role at target performance, combining base salary and annual incentive opportunity. Actual earnings may be higher or lower depending on individual performance and overall company results.
The base pay range reflects the compensation component of this opportunity. The candidate’s actual pay will be based on various factors, such as work location, qualifications, and experience, so the actual starting pay will vary within this range. To find the pay range for this role based on hiring location, click here. 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. Corporate employees are eligible for a year-end bonus based on individual and/or company 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. The incentive component included in the Total Target Cash Earnings Opportunity reflects target performance. Actual earnings included in the Total Target Cash Earnings Opportunity reflect target performance; actual payouts may vary.
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 12 paid 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 also 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 www.t-mobilebenefits.com*.*
Never stop growing! ###
As part of the T-Mobile team, you know the Un-carrier 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. By applying for this career opportunity, you’re living our values while investing in your career growth–and we applaud it. You’re unstoppable! ###
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, genetic information, 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 [email protected] 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.
Salary Context
This $127K-$275K range is above the median for Data Scientist roles in our dataset (median: $167K across 150 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At T-Mobile, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($201K) sits 6% above the category median. Disclosed range: $127K to $275K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
T-Mobile AI Hiring
T-Mobile has 7 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Bellevue, WA, Overland Park, KS, US, Bellevue, WA, US. Compensation range: $160K - $280K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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