Data Scientist Level V / Machine Learning Engineer Level V

Title: Data Scientist Level V / Machine Learning Engineer Level V

Location: Preferred in San Jose, CA, or open in Salt Lake City, UT / Austin, TX

Duration: 6 Months + With possible extension

Onsite Requirement: No remote work, 3 days onsite

Experience: 10+ Years

HM Notes:

This current team is consisting of 5 Data Engineers working on eBay Security data, which spans into Petabytes. They lack a Data Scientist / Machine Learning Engineer and seek someone to bridge this gap. The ideal candidate will assist the team in building Client models on Security Data.

Mandatory Skills:

Python as a Programming Language

Very strong experience in building Client models

Hadoop Experience

Experience working with Security data & Fraud detection

Dealt with a significant amount of Streaming Data

Nice to have - Winning Combination:

Experience in Generative AI

Experience in security Software Development

Experience in Real-Time Data Processing

We request all candidates to provide a detailed summary answering the following question, which should be prominently displayed at the top of each submitted resume:

"In his/her previous roles - What is the anomaly or a fraud case you were trying to solve using Client models? and how did you solve the problem statement? "

Please prioritize sourcing top-notch quality resumes for this role and share them with me at your earliest convenience.

Day to Day Responsibilities of this Position and Description of Project:

Required Skill Sets:

Experience with scientific computing language and big data knowledge, including Python, SQL, Hive, Hadoop, Spark etc.

Experience with common machine learning algorithms (SVM, KNN, logistic regression, random forest, XGBoost, Neural Networks, etc.)

Develop and maintain Client/Stats models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.

Developed skills in the application of scientific methods to practical problems through exploratory data analysis, hypothesis testing and data visualization to reach robust conclusions.

Understanding of statistical probability distributions, bias, error and power as well as sampling and resampling methods.

Expertise in the manipulation, integration, processing and interrogation of large datasets. Maintain data quality and support data access.

Experience with source control tools such as GitHub and related CI/CD processes

Ability to tackle ambiguous and undefined problems and thrive with minimal oversight and process.

Ability to communicate and discuss complex topics with technical and non-technical audiences

Better to have or willing to learn:

Leverage data to inform strategic directions of safety signal development, aid in incident response, automate detection and enforcement, and provide intelligence on ecosystems.

Operationalize and evolve Threat Detections metrics that measure the impact of our targeted enforcement.

Feel comfortable with analyzing and telling stories of security, audit and network data, and understand the context of monitor systems and threat detection.

Disseminate intelligence gathered to other safety stakeholders and executive decision makers.