We are seeking a highly analytical Data Scientist with experience in deep learning, machine learning, knowledge graph, and large language models. The ideal candidate will be responsible for curating large amounts of raw data to find patterns to enhance our product line with machine learning. They will build data products to extract valuable insights and use data enrichment techniques to support graph databases and large language models. The candidate should have critical thinking and problem-solving skills essential for interpreting data and a passion for machine-learning and research.
Requirements:
- Bachelor's Degree or Higher in one of the following disciplines: Operations Research, Applied Mathematics, Engineering, Science, Computer Science, Mathematics, Statistics or GIS.
- Understanding of machine learning, deep learning and modeling tools.
- Experience with semantic knowledge graphs and large language models
- Experience interacting with “big data” systems such as Microsoft Azure Data Lake, Elastic Cloud, and/or Amazon Web Services (AWS).
- Experience with various data ingestion techniques and script writing for data ingestion.
- Proficient in one or more programing languages: R, Python, Java, Scala, HTML, Matlab, R, SQL, C/C++, and JavaScript.
- Proficient in one or more Libraries: numpy/scipy, scikit-learn, pandas, PyMC3, Ray/RLLib, Theano, TensorFlow, PyTorch, Caffe, Keras, pyspark, OpenCV, AngularJS, D3.js, jQuery, Boost (C++).
- Proficient in Software/Frameworks: Power Apps, Power Automate, ArcGIS Platform, Docker, Kubernetes, Kibana, Logstash, Node.js, YARN, Zookeeper, HDFS, Apache Spark, Apache Kafka, Ambari, Git, MS Office (Word, Powerpoint, Excel), Unix/Linux, OpenStack, AWS, Azure, VMWare.
- Proficient in Databases: Dgraph, ArgangodB Elasticsearch, PostgreSQL/PostGIS, MySQL, MongoDB, Redis, Accumulo.
- Proficient in Geospatial Information Systems (GIS) software (e.g., ESRI ArcGIS® suite) to support data analysis in varying domain classifications.
- Knowledge of statistical/machine learning algorithms.
- Knowledge of digital rights management.
- Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis.
- Knowledge of programming language structures and logic.
Preferred:
- Degree in one of the following disciplines: Applied Mathematics, Engineering, Science, Computer Science, Mathematics, Statistics or GIS.
- Geospatial Information Systems (GIS) software (e.g., ESRI ArcGIS suite).
- Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.
- Knowledge of cybersecurity principles; cyber threats and vulnerabilities; specific operational impacts of cybersecurity lapses.
- Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).
- Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.