We are seeking a highly skilled and motivated Senior Data Scientist to join our talented team. As a Senior Data Scientist, you will play a key role in developing and optimizing our machine learning and LLM capabilities.
You will work closely with software engineers, data scientists, security researchers, and product management to design, develop, evaluate, and deploy machine learning models and state-of-the-art LLM-based features that address complex cybersecurity challenges.
Responsibilities:
Analyze large, complex datasets to identify patterns and trends that could indicate potential security threats.
Collaborate with cybersecurity researchers to understand emerging threats and develop solutions that leverage advanced machine learning and data analytics.
Design, develop, and evaluate ML and LLM-based solutions, including classification, NLP, and security-focused models, from experimentation through production.
Develop and optimize LLM techniques including prompting, retrieval, embeddings, RAG, and fine-tuning where appropriate.
Design robust evaluation methodologies and experiments for classification and LLM-based systems, using appropriate metrics to measure model quality, accuracy, and performance.
Stay up to date with advancements in machine learning, NLP, LLMs, and related technologies, and evaluate their applicability to real-world cybersecurity problems.
Collaborate with software and data engineers to integrate ML and LLM solutions into scalable production systems.
Take ownership of complex data science problems, from problem definition and research through implementation and evaluation.
Requirements: Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or a related field. A Masters or Ph.D. is a plus.
5+ years of hands-on experience in data science, machine learning, or a related field.
Strong understanding of machine learning and deep learning, with hands-on experience developing and evaluating classification models.
Hands-on experience with NLP and text classification, including NER, training and fine-tuning models, and working with both traditional ML and modern LLM-based approaches.
Strong proficiency in Python. Experience with PySpark and Databricks is a plus.
Experience developing and evaluating LLM-based applications, including prompt engineering.
Experience with embeddings, vector search, and vector databases.
Experience working with commercial and open-source LLMs and frameworks, such as OpenAI models and the Hugging Face ecosystem.
Experience with model fine-tuning, RAG systems, and LLM-based classification.
Experience working with highly imbalanced datasets, selecting appropriate evaluation metrics, and optimizing models for precision/recall trade-offs.
Strong analytical and problem-solving skills, with the ability to evaluate and interpret complex data and experimental results.
Excellent communication and collaboration skills, with the ability to work effectively in a multidisciplinary team.
Proven track record of delivering high-quality solutions in a fast-paced, dynamic environment.
This position is open to all candidates.