Data Analytics Senior Consultant - Data Lake

Deloitte Deloitte · Consulting

This role focuses on building and modernizing cyber data and analytics programs, leveraging AI and ML for cyber defense and operations. It involves working with cyber security cloud platforms, data platforms, and AI development tools like vector databases and frameworks. The role requires experience in statistical analysis, machine learning, data mining, and preparing data for analysis using languages like Python and SQL, with a focus on cyber specific use cases.

What you'd actually do

  1. Design and modernize large scale cyber data and analytics programs that promote organizational intelligence, provide embedded capacity, and implement as-a-service based subscription models at scale
  2. Harness the potential of bleeding edge cyber big data and AI technologies such as Databricks for Cyber, AWS Security Lake, Google Sec Ops and the latest from traditional security providers like Splunk, Crowdstrike, Palo Alto and others.
  3. Enable day-to-day operations, maintenance and ongoing enhancements of their data platforms and applications as well as managing and governing the underlying data leveraging standardized, automated and AI enabled Data Ops capabilities
  4. Mature their AI and Analytics journey with fluid capacity and flexible capability of AI and Analytics experts complemented with experience hardened assets and curated data sets to experiment with AI and scale their AI and Analytics ambition.

Skills

Required

  • 2+ years of relevant Analytics consulting or industry experience
  • 2+ Experience with AI development tools such as vector databases (Pinecone, Elastic, etc.) and AI development frameworks (Langchain, CrewAI, etc.)
  • 2+ years experience in statistical analysis, machine learning, and data mining techniques.
  • 2+ years of experience using statistical computer languages (Python, SQL, R, SAS, etc.) to prepare data for analysis, visualize data as part of exploratory analysis, generate features, and other similar data science driven data handling
  • 2+ years of experience using cyber security cloud platforms (Google SecOps, AWS, Azure, etc.)
  • 1+ years of experience with SOC threat hunting and incident response
  • Demonstrated expertise with one full life cycle analytics engagement across strategy, design and implementation.
  • Bachelor's Degree in Engineering, Mathematics, Empirical Statistics or 4 years equivalent professional experience
  • Ability to travel up to 50%, on average, based on the work you do and the clients and industries/sectors you serve

Nice to have

  • Experience architecting, designing, developing and deploying enterprise data science solutions which include components across the Artificial Intelligence spectrum such as NLP, Chatbots, Virtual Assistants, Computer Vision, and Cognitive Services as well the use of big data tools for the management of massive datasets.
  • Knowledge of the intersection of AI / ML / Advanced Data Engineering and cybersecurity specific use cases for Detection, cyber threat response acceleration.
  • Experience parsing and normalizing cyber or IT specific telemetry datasets
  • Expertise in Python machine and deep learning frameworks and libraries, e.g. PyTorch, Keras, Tensorflow, Scikit-learn, Numpy, SciPy
  • Experience designing and implementing Apache Open Source (Kafka, Storm, Spark) frameworks to process end to end data management life cycle
  • Ability to work independently and manage multiple task assignments.
  • Strong oral and written communication skills, including presentation skills (MS Visio, MS PowerPoint).

What the JD emphasized

  • AI development tools such as vector databases (Pinecone, Elastic, etc.) and AI development frameworks (Langchain, CrewAI, etc.)
  • statistical analysis, machine learning, and data mining techniques
  • statistical computer languages (Python, SQL, R, SAS, etc.)
  • cyber security cloud platforms (Google SecOps, AWS, Azure, etc.)
  • SOC threat hunting and incident response
  • enterprise data science solutions which include components across the Artificial Intelligence spectrum such as NLP, Chatbots, Virtual Assistants, Computer Vision, and Cognitive Services
  • AI / ML / Advanced Data Engineering and cybersecurity specific use cases

Other signals

  • AI and ML for cyber defense and operations
  • AI and Analytics journey
  • AI and Analytics ambition