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Open nowPosted 18 hours ago

Data Scientist

PUNCH Cyber Analytics Group3 open roles

Where
Reston, Virginia, United States, Remote
Work mode
Remote
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Your applicationOpen nowData ScientistPUNCH Cyber Analytics Group · Reston, Virginia, United States, Remote

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This job: posted 18 hours ago

The posting

About Us:

PUNCH Cyber Analytics Group (PUNCH) is a Virginia-based, small business founded in 2012 operating as a cohesive team that incorporates the sum of our group’s diverse skills, talents, and resources toward our collective passion: advancing data analytics to impact cyber operations. PUNCH is a two-time Inc. Magazine ‘Best Workplaces’ awardee offering unique benefits and personal touches to provide a positive work-life experience for our team. PUNCH brings unique qualifications, resources, and past-performance that make us suitable to address the goals of our diverse customer-base. Further, we have past and current experience supporting cyber operations and cyber ML-based research, with well over 100 years of collective experience from our collaborative, multi-disciplinary team.

Responsibilities

  • Develop and evaluate machine-learning analytics for cyber defense use cases using network, sensor, alert, asset, and other operational telemetry.
  • Build unsupervised and statistical models for clustering, anomaly/outlier detection, behavioral baselining, novelty detection, and pattern discovery.
  • Apply techniques such as graph analytics/embeddings, nearest-neighbor methods, time-series or periodicity analysis, clustering, dimensionality reduction, and anomaly scoring to large cyber datasets.
  • Design models and features that account for concept drift, noisy data, incomplete ground truth, and high false-positive rates common in operational cyber environments.
  • Support asset discovery and entity resolution, including development of probabilistic asset graphs that associate IPs, hostnames, MAC addresses, services, certificates, device attributes, and other observations across data sources.
  • Develop contextual features from security alerts and network telemetry, including temporal patterns, rarity/frequency, communication behavior, entity context, and related activity, and use those features to identify meaningful alert clusters and outliers.
  • Work with cyber analysts and detection engineers to turn operational questions and adversary behaviors into measurable features, experiments, and analytics.
  • Evaluate model effectiveness using appropriate quantitative metrics and operational validation; benchmark accuracy, false-positive behavior, computational performance, and usefulness to analysts.
  • Develop production-quality Python code and work with engineers to integrate models into sensor-side CPU environments as well as larger GPU-enabled enterprise analytics platforms.
  • Understand the practical strengths and limitations of LLMs: know when to use an LLM, when to use conventional ML/statistics, and when a deterministic rule or query is the better answer.
  • Ability to build evaluation harnesses rather than judge AI output by vibes—test datasets, expected behaviors, regression tests, failure cases, and quantitative measures.

Qualifications

  • BS or MS in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, Cybersecurity, or a related quantitative discipline.
  • Strong Python skills and hands-on experience with common scientific/ML tooling such as pandas, NumPy, scikit-learn, SciPy, and related libraries.
  • Strong understanding of unsupervised machine learning, including clustering, anomaly/outlier detection, similarity/distance methods, feature engineering, and statistical baselining.
  • Experience with at least some of the following: graph analytics or graph ML, entity resolution/record linkage, probabilistic modeling, time-series analysis, change-point/concept-drift detection, nearest-neighbor methods, or dimensionality reduction.
  • Experience working with large, noisy, heterogeneous datasets where labels or authoritative ground truth are limited.
  • Familiarity with scalable data processing and efficient model implementation; comfortable thinking about CPU/memory constraints as well as GPU acceleration for larger workloads.
  • Working knowledge of networking and cybersecurity concepts such as IP addressing, DNS, TLS, network flows, ports/services, routing, network devices, and security alerts.
  • Experience with cyber/network telemetry such as Zeek, PCAP-derived data, SIEM data, IDS/IPS alerts, device configuration data, or vulnerability/asset data is highly desirable.
  • Experience with graph/network-analysis libraries, SQL/data stores, Elasticsearch/Splunk, or similar analytic platforms is a plus.
  • Experience developing analytics for cybersecurity, threat hunting, detection engineering, or defensive cyber operations is strongly preferred.
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