Role Description:

The Data Scientist is responsible for developing and implementing AI-driven solutions to enhance cybersecurity measures within the organization. This role involves leveraging data science techniques to analyze security data, detect threats, and automate security processes. The Data Scientist will work closely with cybersecurity teams to identify data-driven automation opportunities, strengthening the organization’s security posture.

Roles & Responsibilities:

• Develop analytics to address security concerns, enhancements, and capabilities to improve the organization’s security posture.

• Collaborate with Data Engineers to translate security-focused algorithms into effective solutions.

• Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.

• Perform exploratory and targeted data analyses using descriptive statistics and other methods to identify security patterns and anomalies.

• Design and implement security-focused analytics pipelines leveraging MLOps practices.

• Collaborate with data engineers on data quality assessment, data cleansing, and the development of security-related data pipelines.

• Contribute to data engineering efforts to refine data infrastructure and ensure scalable, efficient security analytics.

• Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes.

• Share and discuss findings with team members practicing SAFe Agile delivery model.

Basic Qualifications:

• Master’s degree and 1 to 3 years of experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python) OR

• Bachelor’s degree and 3 to 5 years of experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python) OR

• Diploma and 7 to 9 years of experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python)

Preferred Qualifications:

• Experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python)

• Demonstrated skill in the use of applied analytics, descriptive statistics, feature extraction and predictive analytics on industrial datasets

• Strong foundation in machine learning algorithms and techniques

• Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification

Good-to-Have Skills:

• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)

• Outstanding analytical and problem-solving skills; Ability to learn quickly; Excellent communication and interpersonal skills

• Experience with data engineering and pipeline development

• Experience in analyzing time-series data for forecasting and trend analysis

• Experience with AWS, Azure, or Google Cloud

• Experience with Databricks platform for data analytics and MLOps

• Experience with Generative AI models (e.g., GPT, DALL·E, Stable Diffusion) and their applications in cybersecurity and data analysis

• Experience working in Product team’s environment

• Experience working in an Agile environment

Professional Certifications:

• Any AWS Developer certification (preferred)

• Any Python and ML certification (preferred)

• Any SAFe Agile certification (preferred)

Soft Skills:

• Initiative to explore alternate technology and approaches to solving problems

• Skilled in breaking down problems, documenting problem statements, and estimating efforts

• Excellent analytical and troubleshooting skills

• Strong verbal and written communication skills

• Ability to work effectively with global, virtual teams

• High degree of initiative and self-motivation

• Ability to manage multiple priorities successfully

• Team-oriented, with a focus on achieving team goals