Lead Data Scientist

Role Purpose Key Accountabilities:

The primary role of a Lead Security Data Scientist at Prevalent AI involves analyzing significantly large volumes of security telemetry data collected from various sources (application logs, network logs, database logs, cloud and perimeter security devices), implement complex Machine Learning algorithms and building advanced models, applying these concepts to real security data sets in single or clustered environments, drawing inferences from the collated data and preparing conclusions out of it, to analyse and assess security risk posture for enterprises.
This role also involves leading a talented team of Data Scientists who employ sophisticated analytics programs, data mining techniques, machine learning and statistical methods to prepare data for use in predictive and prescriptive modelling.

Key accountabilities include:

Developing a firm understanding of the essential business requirements through interaction with, and interrogation of, business SMEs and translating that understanding into models that address the requirements, using data science methods, including:

Data assembly, cleansing, validation

Data visualization

Statistical modelling, supervised and unsupervised machine learning, mathematical programming

Inspecting data to confirm that it is consistent with expectations, modifying designs as necessary.

Communicating predictions and findings to the Organization through effective data visualizations and reports.

Creating prototypes of key data manipulations, visualizations and mathematical modelling elements.

Validating designs with business SMEs via discussions, examples, prototype demonstrations and documentation and iterating designs in response to negotiations with business SMEs.

Conveying the designs to the development teams via discussion, documentation and prototype code.

Developing an understanding of industry trends and best practices.

Creating and follow personal education plan in the technology stack and solution architecture.

Experience Skills:
Exposure to cyber security domain.

Extensive relevant experience in Data Science.

Hands on experience in production deployment of ML models in large complex environments.

Ability to learn quickly in a fastpaced environment.

Excellent communication and team management skills.

Selfmotivated individual capable of working in a fast-paced environment.

Great verbal and written communication skills.

A strong analytical mind-set.

Knowledge:
Good understanding and expertise in processing large datasets.

Good working knowledge in implementation of various ML algorithms in big data environment

Understanding of data visualization tools, such as Tableau, etc.

Familiarity in working with Big Data and common data science tools such as Python, Spark etc. Proficiency in at least one.

Familiarity with Scala, HIVE is desired.

Good scripting and programming skills; SQL proficiency.

Understanding of and proficiency in NLP.

Understanding of a range of statistical and machine learning techniques and algorithms, such as KNN, decision trees, SVM, CNN, etc.

Good understanding of the process workflow:

Problem definition to hypothesis building

Prototype model building using python/Scala

Implementation of ML problems in big data.

Performance Monitoring Evaluation of System

Education:
Master s in Engineering, Science, or Mathematics

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