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Analyze and investigate existing detection logic built on multiple interconnected signals and rule-based expressions
Understand and map complex data flows, processing pipelines, and dependencies across enterprise systems
Identify redundancy, inefficiencies, and unnecessary complexity within current detection structures
Propose simplification and optimization approaches while preserving detection quality and operational effectiveness
Conduct exploratory analysis on large and complex datasets to validate hypotheses and support decision-making
Assess downstream implications and operational risks of proposed changes before implementation
Translate analytical findings into clear recommendations for technical and non-technical stakeholders
Collaborate closely with engineers, data scientists, and security researchers to gather context and resolve blockers independently
Operate effectively in a fast-paced environment with shifting priorities and short delivery cycles
Contribute to improving analytical methodologies and approaches within the newly forming team
5+ years of experience in Data Science, Analytics Engineering, Data Analytics, or related analytical roles
Strong proficiency in Python and SQL
Experience working with large-scale and complex datasets
Strong analytical and problem-solving skills with the ability to independently structure ambiguous problems
Proven experience driving investigations from hypothesis generation to data-backed conclusions
Strong engineering mindset with the ability to understand and reason about complex logic and codebases
Ability to evaluate system behavior and understand downstream impacts of analytical decisions
Experience collaborating cross-functionally with engineering and research teams
Ability to work autonomously with minimal supervision
Upper-Intermediate or higher English level
Familiarity with JavaScript
WILL BE A PLUS
Experience with Snowflake
Experience in cybersecurity, fraud detection, or risk analytics domains
Experience working with rule-based systems or detection pipelines
Exposure to enterprise-scale analytics platforms
Understanding of data pipeline architecture and signal processing systems
PERSONAL PROFILE
Independent and proactive problem solver
Curious and investigative mindset
Comfortable working in ambiguous environments
Strong ownership mentality
Adaptable to changing priorities and fast delivery cycles
Collaborative and communication-oriented
Detail-oriented with strong systems thinking capabilities
Are you passionate about solving complex data challenges beyond traditional Machine Learning models? We are looking for a Senior Data Scientist to join a newly forming team focused on improving large-scale analytics and detection systems within a cybersecurity product ecosystem.
As part of Sigma Software , you will collaborate with engineers, researchers, and analytics experts to simplify complex detection logic, investigate enterprise-scale data flows, and drive impactful improvements across production systems. This is a Full-time remote/hybrid opportunity for specialists who enjoy autonomy, systems thinking, and solving ambiguous analytical problems.
We at Sigma Software offer the opportunity to work on technically sophisticated products, influence analytical practices from the early stages, and collaborate with experienced international teams in a fast-paced environment.
CUSTOMER
Our Customer is a global cybersecurity company delivering enterprise-grade security and risk mitigation solutions for organizations and digital platforms worldwide. The company operates in a highly data-driven environment where large-scale signal processing, detection systems, and operational intelligence play a critical role in protecting users and businesses against evolving digital threats.
PROJECT
The project focuses on improving and simplifying a sophisticated analytics and detection ecosystem within the Customer’s Enterprise product line. The team works with complex rule-based detection logic and large-scale signal processing systems to identify redundancy, optimize data flows, and improve operational efficiency without compromising effectiveness.
This is not a Machine Learning-focused position. Instead, the role requires deep analytical thinking, engineering understanding, and the ability to investigate complex live systems independently. You will work closely with engineers, data scientists, and security researchers in a dynamic environment with rapid iteration cycles and evolving priorities.
