Job Description
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!
As a Data Scientist, you are responsible for applying machine learning techniques and statistical methods to solve business problems. You will collaborate with a multi-disciplinary team of technical and non-technical business partners on a wide range of challenges. You will work across the entire machine learning (ML) lifecycle, including problem framing, data collection, exploratory data analysis, model development, deployment, and performance measurement. Paramount to this job is the understanding that a Data Scientist's ultimate goal is to create real business value from data.
Responsibilities:
- Extract, prepare and model large, complex data sets using a combination of skills, including machine learning theory, mathematics, statistics, and programming.
- Perform data mining and statistical analyses on ad hoc business problems.
- Define problems in mathematical terms. For example, “Why is product feature X underperforming?” and “What data on user behavior might help explain it?”
- Communicate important information and insights to business leaders using verbal, written, and data
- visualization skills.
- Also responsible for other Duties/Projects as assigned by business management as needed.
Qualifications:
- 2-4 years Industry experience in predictive modeling, data science, and analysis in an ML engineer or data scientist role.
- 2-4 years Experience with data scripting languages (e.g., SQL, Python, R)
- 2-4 years Experience in data visualization
- 2-4 Experience developing statistics-based analytics reports
- 2-4 years Experience in the telecom industry preferred
- Calculus, linear algebra, statistics, and probability
- Working knowledge of Python and SQL
- Machine Learning: Solid understanding of machine learning concepts and techniques related to supervised and unsupervised learning
- Bachelor’s Degree in Quantitative Discipline (math, statistics, economics, computer science, physics, engineering) required
- Masters or Advanced Degree in Quantitative Discipline (math, statistics, economics, computer science, physics, engineering) preferred
- 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
Base Pay Range: $86,300 - $155,700
Corporate Bonus Target: 15%
The pay range above is the general base pay range for a successful candidate in the role. The successful 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.
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. To find the pay range for this role based on hiring location, copy and paste this link into your browser:
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
. 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, 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, Local area, Flexible hours,