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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.
Job Responsibility:
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
Requirements:
Bachelor’s degree in Mathematics, Statistics, Economics, Computer Science, Physics, Electronic Engineering, or related, and 5 years of relevant work experience
Master’s degree in Mathematics, Statistics, Economics, Computer Science, Physics, Electronic Engineering, or related, and 3 years of relevant work experience
Experience in developing and deploying predictive models, advanced machine learning, deep learning, NLP, and generative AI solutions by applying a wide range of algorithms
Experience in developing solutions using Python, PySpark, SQL, and R, with libraries LangChain, LangGraph, Keras, Pandas, NumPy, SciPy, Matplotlib, and Scikit-Learn
Experience in 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
Experience in 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
Experience in 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
Experience in 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