Sr Data Scientists

T-Mobile•Frisco, TX

41d•Hybrid

About The Position

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! T-Mobile is America’s supercharged Un-carrier, delivering an advanced 4G LTE and transformative nationwide 5G network that will offer reliable connectivity for all. Sr Data Scientists is located in Frisco, TX and will support teams’ mission to partner with leaders across T-Mobile to understand the “art of the possible” and identify AI/ML opportunities to grow our business, reduce costs, manage risk, detect anomalous behavior, forecast/predict outcomes, and delight our customers. Telecommuting is permitted, but applicant must work from the worksite location at least 3-4 days per week. No additional national or international travel is anticipated.

Requirements

PRIMARY REQUIREMENTS: Bachelor’s degree in Mathematics, Statistics, Economics, Computer Science, Physics, Electronic Engineering, or related, and 5 years of relevant work experience in any occupation in which the required experience is gained.
ALTERNATIVE REQUIREMENTS: Master’s degree in Mathematics, Statistics, Economics, Computer Science, Physics, Electronic Engineering, or related, and 3 years of relevant work experience in any occupation in which the required experience is gained.
Developing and deploying predictive models, advanced machine learning, deep learning, NLP, and generative AI solutions by applying a wide range of algorithms, including regression linear and logistic, decision trees, random forests, gradient boosting (GBM), clustering and segmentation, naive Bayes, support vector machines, deep neural networks, LLMs, RAG, fine-tuning, prompt engineering.
Developing solutions using Python, PySpark, SQL, and R, with libraries LangChain, LangGraph, Keras, Pandas, NumPy, SciPy, Matplotlib, and Scikit-Learn.
Working with data querying, wrangling, cleaning, and feature engineering across relational and non-relational databases: SQL, Snowflake, and Redshift in big data environments: Azure, AWS, and GCP, and leveraging Spark, Hadoop, Hive, and Kafka.
Building CI/CD pipelines, automating training and retraining workflows, deploying inference services, and monitoring ML algorithms in production environments in Databricks using tools: MLflow, and cloud-native services.
Articulating and reframing business problems, applying statistical and advanced analytics techniques in Python, R, and SQL, and leveraging SciPy, Scikit-Learn, and PySpark to generate actionable insights and recommendations.
Delivering impactful, data-driven presentations and effectively communicating machine learning and analytical concepts to technical teams, business stakeholders, and senior leadership, supported by visualizations created in Tableau, Power BI, Matplotlib, and Seaborn.
At least 18 years of age
Legally authorized to work in the United States

Responsibilities

Support business partners and product owners to understand business challenges, develop business cases, capture requirements, co-create solutions that drive business change that solve the challenges and deliver impactful business outcomes.
Provide senior-level guidance and mentorship to the data science team, including reviewing projects, models, and code for peers and junior team members.
Design advanced analytics to solve business problems; preprocess and perform exploratory data analysis on structured and unstructured data; create features based on expertise in the domain; use predictive modeling techniques and statistical analysis to predict outcomes and behaviors.
Leverage the Agile methodology to ensure alignment of data science roadmap, features, and stories to business priorities and value streams.
Collaborate with cross-functional team comprised of other data scientists, data engineers, ML engineers, and data analysts.
Partner with other technology partners such as architects, engineers, product managers, scrum masters, release train engineers, and agile coaches to deliver on targeted business outcomes.