Data Scientist II

Data Scientist II

Requirements

• Bachelor’s degree, or foreign equivalent, in Computer Science, Mathematics, Statistics, Engineering (any field) or closely related quantitative discipline
• two (2) years of experience in job offered or in any occupation in a related field
• (1) Python
• (2) R
• (3) Statistical Modeling
• (4) Linear/Logistic Regression
• (5) HTML
• (6) JavaScript
• (7) Structured Query Language (SQL)
• (8) Machine Learning
• (9) Data Mining and Visualization
• (10) Tableau, PowerBI, or Kibana
• (11) Preparation and delivery of technical presentations leveraging advanced data analytics for executive level audiences

Responsibilities

• Perform data mining and analytics on F5 production data to gain insight into cybersecurity risks and threat actors targeting F5 customers
• Identify and characterize automated threats against F5 customers’ applications and present threat summaries and proposed countermeasures to customer executive, security, and related teams
• Conduct interview sessions with the customer management teams to understand the client’s requirements, designing data analysis workflow, and conceptualizing the business requirements to mathematical and statistical questions
• Extract, transform, and load large amounts of network data using database management tools; use advanced SQL queries to manipulate data from various databases such as Google Cloud Platform and generate consolidated data sources for further analysis
• Using Python to clean and process the network data, engineer transaction features, and extract key information from unstructured data; identify false positives and missed automated traffic; recognize the automation trends using entropy analysis, time series analysis, probability distribution and P-value calculation
• Conducting root cause analysis on unusual traffic data patterns and security incidents; identify individual web automation campaigns using manual heuristics and use of scripts to discover clusters of low-randomness data and exploring similarities among distinct automation groups using hypothesis testing
• Using sampling methods to split data into training and testing datasets; fit the machine learning models to make predictions, validating the model results and tune parameters and evaluating the performance of machine learning models and compute the requisite evaluation metrics
• Visualizing network data using Kibana, Python, and custom tooling to identify anomalies; explore patterns and seasonality of the automated transactions and developing customized graphs and visual dashboards to address client requests and share insights with clients
• Translating and interpreting analytical findings into compelling business insights, compiling professional slides by using Google Slides or Powerpoint and present statistical results and recommendations to senior executives with evidence-based reasoning and accessible storytelling
• Compiling case studies on attack techniques and fraud schemes to be used by marketing and sales representatives, as well as for reference use in customer briefings and internal trainings
• Presenting reports to senior executives at F5’s customers and gathering client feedback to improve and advance team reporting processes and standards

Preferred Qualifications

No preferred qualifications provided.