Data Scientist - Assurance Machine Learning Engineer

Position: Data Scientist - Performance Assurance Machine Learning Engineer

Contract &

Location:

W2 contract, minimum 6 months, onsite in Plano, TX.
Target hourly rate: $75-82/hr.

As a Performance Assurance Machine Learning Engineer, you will work under the coaching of senior and lead engineers within the Data Science & Tools Team to analyze the company’s deployed 4G/5G network elements. You will extract and query data, develop predictive models, and build dashboards to identify infrastructure and performance issues and communicate findings to stakeholders.

• Work closely with internal and external stakeholders to explore relationships of 4G/5G KPI measurements and targets for product acceptance and performance monitoring.
• Collaborate with RF engineers, network engineers, data scientists, platform engineers, product teams, and operations stakeholders to ensure machine‑learning outputs are technically accurate, interpretable, and operationally useful.
• Utilize A/B testing, statistical, and machine‑learning models to build robust mechanisms for product & feature performance analysis, evaluation of new product releases, and third‑party product evaluation.
• Assist in product & feature performance analysis and evaluation of new product releases and third‑party product evaluation using analytics to drive intelligent business decisions.
• Proactively define and interpret data, metrics, and KPIs, analyze results, and provide insights on operational impact, trends, and opportunities for all 4G/5G RAN products.
• Prototype use cases, implement automations, and develop tools to support and augment manual or repetitive efforts.
• Communicate key findings to stakeholders using visualizations and other suitable methods.

• Experience: 7+ years professional experience in Data Science, Analytics, or Engineering; 3–5 years designing, deploying, and maintaining large‑scale ML systems in production.
• Programming: Proficiency in Python & Spark (preferred); R, SQL, Hive, JavaScript, Visual Basic, C++, or shell scripting in a Linux environment.
• Cloud Development: Experience with AWS, Azure, or Google Cloud and data platforms such as Databricks or Snowflake.
• Machine‑Learning Expertise: GLM regression, decision trees (including boosted trees and random forest), k‑means and hierarchical clustering, PCA, t‑SNE, neural nets (transformers, auto‑encoders), Bayesian regression, and time‑series modeling.
• Large‑Scale Data: Handling high‑volume (1TB+) and high‑dimensional datasets (500+ variables) within big‑data frameworks like Hadoop, Citus, or MongoDB.
• Data Engineering: Data wrangling, exploratory data analysis, correlation analysis, statistical methodologies, significance testing, A/B testing.
• MLOps & Dev Ops: Knowledge of MLFLOW, Docker, Kubernetes, Kube Flow, CUDA, and Linux administration.
• Version Control: Experience with Git and platforms such as Git Hub or Bitbucket.
• Educational Background: Graduate degree in Computer Science, Statistics, Data Science, or related field preferred.
• Additional Knowledge: Wireless infrastructure experience (e.g., CDMA, EVDO, LTE, VoLTE, 5G), evaluation of service performance trends, and experience with RAN system performance issues.
• Soft Skills: Strong analytical and problem‑solving abilities, independence, teamwork, integrity, excellent oral and written communication, and curiosity to learn.