- Experience in transferring applied research to technologies.
Experience in cyber security concepts such as malware detection, fraud prevention, adversary tradecrafts, emerging threats.
Design, develop, and implement scalable data pipelines using Spark/PySpark and big data frameworks to ingest, transform, and load data.
Optimize and troubleshoot complex queries for efficient data retrieval and performance.
Build and deploy AI-ML models across diverse data sources to extract valuable insights.
Familiar with modern tools for data exploration and analysis.
Collaborate with data scientists, analysts, and stakeholders to understand business needs and translate them into actionable data solutions.
Document code and processes for clarity and knowledge transfer.
- Bachelor s/Masters degree in Data Science, Statistics, Computer Science, or a related field (a plus).
At least 5 years of experience working in query tuning, performance tuning, implementing and debugging Spark/Pyspark and other big data solutions.
Experience with anomaly detection, clustering statistics, time series analysis, reinforcement learning, generative AI/agentic frameworks, large language models.
Experience with cloud platforms like AWS or GCP and containerization technologies like Docker and Kubernetes.
Strong problem-solving, analytical, and critical thinking skills.
Excellent communication and collaboration skills.
Ability to work independently and as part of a team.
