Senior Machine Learning Researcher

Overview

As a Senior Machine Learning Researcher specializing in security, apply machine learning and cybersecurity expertise to develop solutions that detect, prevent, and respond to security threats and fraud. Conduct independent research, collaborate with cross-functional teams, and stay updated with advancements in machine learning and cybersecurity.

What you'll do

• Develop and implement machine learning and deep learning models to detect and mitigate security threats.
• Develop new product features using statistical and machine learning-based algorithms to identify bot and fraud traffic.
• Conduct data cleaning, preprocessing, and exploratory data analysis.
• Apply statistical methods to analyze security-related data.
• Stay updated with the latest research and advancements in machine learning and cybersecurity.
• Publish research findings in reputable journals and conferences.
• Collaborate with cross-functional teams to understand project requirements and contribute to the development of solutions.
• Collaborate with security analysts, software engineers, and other stakeholders to integrate machine learning solutions into security products.
• Communicate complex technical concepts effectively to technical and non-technical stakeholders.

What you'll need

• Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Proficiency in machine learning algorithms, neural networks, and model development.
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
• Strong coding skills in Python and R, and familiarity with languages such as C++, Java, or Scala.
• Ability to perform data cleaning, preprocessing, and exploratory data analysis.
• Strong understanding of statistical methods and their applications.
• Familiarity with cybersecurity concepts, threat models, and common attack vectors.
• Experience with security tools and technologies such as intrusion detection systems, firewalls, and malware analysis tools.
• Strong analytical and problem-solving skills to identify and mitigate security threats using machine learning techniques.
• Understanding of network security, bot security, application security, and incident response.

Nice to have

• Experience with bot detection, device fingerprinting, behavioral biometrics, or real-time risk scoring.
• Experience with low-latency, high-throughput machine learning serving infrastructure.
• Familiarity with current machine learning research relevant to fraud and security, such as following or contributing to arXiv publications in adversarial machine learning, anomaly detection, or graph learning.
• Experience scaling researcher productivity using Agentic AI.

Details

• Location: Pune, India.
• Hybrid office model with flexible working hours and work-from-home days.