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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in data analysis, machine learning engineering, and building data pipelines, with a strong focus on classification capabilities and model evaluation. Proficient in Python, SQL, and regular expressions, with experience in cloud environments and cybersecurity domains.
Highest-signal resume keywords
Data AnalysisMachine Learning EngineeringPython ProgrammingRegular ExpressionsData Pipeline Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data AnalysisMachine Learning EngineeringPython ProgrammingSQLRegular ExpressionsNLP Model Fine-TuningData Pipeline DevelopmentETL ProcessesExperiment DesignModel Quality Measurement
Soft Skills
Problem SolvingSelf-StarterProactive Mindset
Tools & Technologies
SparkAirflowDagsterAWSAzureGCPGitCI/CD
Industry Keywords
B2B SaaSCybersecurityDSPMDLPData Classification
Tech Stack
Tools & technologiesAirflowAWSAzureCloudCyber SecurityETLGoogle Cloud PlatformPythonSparkSQL
About the role
Key responsibilities & impact- Research, design, and ship new classification capabilities expanding coverage of sensitive data types
- Own the end-to-end process from research and experiments through production deployment
- Evaluate and tune regex, dictionary, fine-tuned NER, and text classification models
- Build ground truth sets, measure precision and recall, identify root causes of false positives and false negatives, and fix them
- Build automated evaluation and benchmark workflows to compare classifier versions and catch quality regressions
- Design, build, and maintain data pipelines and dashboards tracking classification quality and product performance
- Deliver actionable insights to stakeholders at all levels
- Support proofs of concept and customers with deep classification analysis
- Turn customer feedback into product improvements
- Participate in discussions about Sentra's AI research objectives, priorities, and solutions within the R&D Data team
Requirements
What you’ll need- 3+ years of hands-on experience in data analysis, data science or ML engineering in a B2B SaaS company
- Proficiency with Python and SQL
- Experience with Spark or similar technologies
- Strong regular expressions skills: write, test and tune patterns, and explain their false positive behavior
- Experience designing experiments and measuring model quality, including precision, recall, and error analysis
- Experience building and maintaining data pipelines, ETL processes, and workflow orchestration tools such as Airflow, Dagster, or similar
- Degree in a quantitative field such as Computer Science, Engineering, Mathematics, or Statistics
- Understanding of software development best practices, including Git, CI/CD, and testing
- Can-do attitude, problem solving skills, self-starter and proactive mindset
- Previous experience in the cybersecurity domain, DSPM, DLP, or data classification is an advantage
- Experience with cloud environments such as AWS, Azure, or GCP is an advantage
- Experience fine-tuning NLP models, such as NER or text classification, or using LLMs in daily work is an advantage
Benefits
Comp & perks- Full-time employment
- Hybrid work arrangement
- Office located near a train station
