Perception Architect

An extraordinarily talented group of individuals work together every day to drive TNS' success, from both professional and personal perspectives.  Come join the excellence!

Overview

The Data Scientist focus specializes in developing and programming methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate insights and solutions for client services and product enhancement. Identifies insights from data and metadata sources, interpreting and communicating findings to management.

Responsibilities

Senior Data Scientist

Overview

The Data Scientist focus specializes in developing and programming Neural Network backbones/architectures, Machine Learning methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate insights and solutions for client services and product enhancement. Identifies insights from data and metadata sources, interpreting and communicating findings to management.

Responsibilities

Have you ever received a phone call or text message from a scammer or other unwanted robocaller?  Has someone called you to get you to give out personal or financial information under suspicious circumstances?  Would you like to know that when you get a phone call that it’s from a trusted source and they are who they say they are?  If you have a passion for building machine learning models and Neural Networks with big impact on everyday lives and have experience with large scale cloud-based data then the TNS Data Office and Data Science team has an amazing opportunity for you in our Polan office.  We are looking for a Senior Data Scientist with 15+ years of experience to join our team and own several horizontal and vertical solutions for classifying and scoring billions of calls every day.

The TNS Data Science team works with terabytes of network transaction, enterprise and network metadata, web and mobile application data that TNS collects on nearly 2 billion wireless and wireline subscribers in the US and Canada. 1.5 Billon calls get processed every 24 hours through our systems. We combine this with direct user feedback from dozens of carrier-branded apps and websites that help us, in real-time, detect and stop spam campaigns with our proprietary machine learning and data processing algorithms.  Some of America’s largest telecom carriers and millions of wireless and wireline subscribers in the US use our mobile apps and data platform to block dangerous callers and alert subscribers to potential spam callers.  We have an aggressive roadmap to incorporate dozens of other data sources and model architectures to expand our product offerings and improve the performance of our services both in terms of accuracy (precision and recall) and performance and scalability.

Responsibilities

• Perform exploratory analysis to understand trends and patterns in data from multiple sources and work with data engineers to design data pipelines to implement features and logic for new or existing machine learning and signaling systems.
• Design machine learning models to score and categorize phone calls and text messages that maximizes the detection of spam or fraudulent calls while ensuring our customers get the calls they want and need.
• Optimize existing designs that include the use of Neural Network transformers into production class algorithms.
• Utilize AWS and on-premise capabilities to develop components of our MLOps infrastructure that can create reusable parts from your designs.
• Collaborate with other data scientists, analysts, and engineering teams to evaluate model performance and deploy ML models to production and measure their performance.

Qualifications

Job qualifications:

• Minimum of Master’s degree, ideally a Ph.D., with demonstrated experience in Predictive Analytics, Statistics, Mathematics, Engineering, Operations Research, Computer Science, or related technical discipline.
• Experience with AWS and private cloud Hadoop datasets using Scala, Spark or Hive as well as SQL.
• Minimum of 5 years of work experience in data modeling using Python, R, or similar statistical package with large datasets in a public or private cloud computing environment.
• Minimum of 3 years of experience in two or more of the following: generalized linear models, regression models, ensemble models, resampling methods, natural language processing, model validation and testing, dimensionality reduction, clustering, feature engineering.
• Demonstrated experience with the full data science lifecycle – exploratory data analysis, visualization, training/test corpus development, model design and evaluation in a large-scale computing environment.

Preferred Qualifications

• Experience with spam or fraud detection using tree-based and neural network architectures for heavily class imbalanced classification, reputation scoring, and outlier detection.
• Expertise in predictive modeling using both supervised and unsupervised learning techniques and measuring model performance.
• Experience in data transformation and encoding large feature spaces into lower dimensions or novel measurements to improve performance and model effectiveness.
• Experience with time-series modeling and predictions.
• Experience in Natural Language Processing (NLP) and text processing methods/models.

Qualifications

10 plus years of experience

If you are passionate about technology, love personal growth and opportunity, come see what TNS is all about!

TNS is an equal opportunity employer. TNS evaluates qualified applicants without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, protected veteran status, disability/handicap status or any other legally protected characteristic.