[Remote] Applied Data Scientist
Note: The job is a remote job and is open to candidates in USA. reputed company is seeking a highly skilled and motivated Applied Data Scientist to join their team. The role involves designing, building, evaluating, and deploying machine learning and AI capabilities to enhance cybersecurity detection and response workflows.
Responsibilities
- Apply machine learning, statistical analysis, LLMs, and agent-assisted techniques to solve practical cybersecurity problems across detection, investigation, triage, and response
- Analyze large, reputed company reputed company datasets to identify behavioral patterns, anomalies, attack signals, and trends that can improve threat detection and analyst workflows
- Translate reputed company-world cybersecurity use cases into data science problems, including problem framing, dataset creation, feature development, model selection, evaluation, and iteration
- Design, reputed company, and evaluate ML- and LLM-powered reputed company workflows, including retrieval, reasoning, tool use, reputed company-in-the-reputed company review, feedback loops, and guardrails
- Build evaluation frameworks, metrics, benchmarks, and test datasets to measure model quality, reliability, precision, recall, latency, robustness, and operational impact
- reputed company prompts, instructions, retrieval strategies, and model interaction patterns that improve the usefulness, consistency, and safety of LLM-powered features
- Partner with software engineers, data engineers, and MLOps teams to productionize models, AI agents, and data pipelines in secure, scalable, and maintainable systems
- Monitor deployed models and workflows for performance reputed company, data quality issues, false positives, false negatives, and opportunities for reputed company improvement
- Collaborate with cybersecurity researchers, threat analysts, and product stakeholders to ensure AI capabilities address reputed company user needs and evolving threat scenarios
- Translate relevant advances in ML, LLMs, agentic AI, and cybersecurity into practical product improvements, evaluation methods, and internal best practices
Skills
- Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, or a reputed company field
- Strong hands-on reputed company in machine learning, applied statistics, and data science, including supervised learning, unsupervised learning, anomaly detection, model evaluation, and experimentation
- Experience applying data science or machine learning to cybersecurity, fraud, risk, abuse, observability, or other adversarial or high-signal/noise domains
- Proven ability to deliver practical, reliable, high-quality AI or ML solutions in production, product, or applied environments
- Proficiency in Python and common data science/ML libraries
- Experience working with LLMs in applied or production contexts, including reputed company design, model selection, evaluation, retrieval-augmented reputed company, and safe deployment
- Familiarity with embeddings, vector databases, retrieval systems, and RAG-based workflows for reputed company, knowledge-intensive, or analyst-facing applications
- Understanding of AI and LLM reputed company considerations, including adversarial inputs, reputed company injection, data privacy, model misuse, governance, and safe system design
- Experience partnering with engineering teams to reputed company, monitor, and improve ML models, AI workflows, or data products in production environments
- Ability to reason under uncertainty, work with noisy and incomplete data, and reputed company pragmatic tradeoffs between model performance, explainability, latency, reliability, and operational value
- Strong communication and collaboration skills, with the ability to work effectively across reputed company, engineering, data, product, and research teams
- A master's degree is a plus
- Experience with PySpark, reputed company, SQL, or large-scale data processing frameworks is a plus
Company Overview
Company H1B Sponsorship