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4001 Embarcadero Drive
Arlington, TX 76014
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Lead Data Scientist
Convergenz, LLC. • Arlington, TX
Posted 2 months ago
We are expanding our efforts into complementary data technologies for decision support in areas that capitalize on intelligent applications enabled with computational learning. Our interests are in enabling intelligent applications and corresponding computation learning processing on large and low latent data sets with elastic cloud architecture techniques on premise. To that end, this role will engage with team counterparts in exploring and deploying technologies for engineering features and creating algorithms that result in models incorporated into intelligent applications. Application use cases are expected to focus on core aspects of our business such as risk management and customer experience. Responsibility also includes coding, testing, and documentation of new or modified scalable analytic data systems including automation for deployment and monitoring. This role participates along with team counterparts to architect an end-to-end framework developed on a group of core data technologies. Other aspects of the role include developing standards and processes for computational learning projects and initiatives.
- Evaluate, research, experiment with computational learning technologies in a lab to keep pace with industry innovation while assessing business impact and viability for use cases associated with efforts in hand
- Work with statisticians, data engineers, application developers, and related groups to inform on and showcase capabilities of emerging technologies and to enable the adoption of these new technologies, computational learning, and associated algorithms
- Work closely with statisticians, data engineers, application developers, other IT counterparts, and business partners to develop, integrate and deploy computational learning as part of applications
- Code, test, deploy, monitor, document, and troubleshoot computational learning processing and associated automation
- Educate and develop system engineers on distributed systems engineering so as to enable future data science and practice
- Perform other duties as assigned
- Conform with all corporate policies and procedures
- Excellent knowledge of Linux, AIX, or other Unix flavors
- Experience with recent computational learning technologies such TensorFlow, Caffe, Torch, Neon, SystemML, or Theano
- Experience with directed analytic graph processing using Beam, Nifi, Flink, and/or Samza
- Experience with messaging technologies such as Kafka, RabbitMQ, ZeroMQ, or MQTT
- Experience with high dimensional visualization using t-SNE or PCA or other related technologies such as Ayasdi
- Working knowledge of cloud based computational learning technologies such as Google Cloud Machine Learning, Microsoft Azure Machine Learning, or IBM Watson
- Working knowledge of Rasa, Spacey/Prodigy, NLTK, Standford CoreNLP, ELMo, and other natural language understanding and processing frameworks and modeling
- Demonstrated strong track record on delivering computational learning based solutions that solve complex analytical problems using quantitative approaches that are a blend of analytical, mathematical and technical skills
- Excellent written and verbal communication skills
- Experienced with solution development, deployment, and/or administration of distributed computational learning and/or analysis systems such as Spark, H2O, SAS Grid, Tensorflow, or Hadoop
- 2-4 years hands-on experience with NoSQL data stores such as MongoDB, Cassandra, HBase, Riak or other technologies that embed NoSQL such as MarkLogic or Lily Enterprise required
- 3-5 years data science experience or similar quantitative skills (statician, actuary) required
- 5-7 years software engineering in languages to include Java, SAS, and Python required
- 5-7 years hands-on experience with SQL databases and Business Intelligence tools such as Oracle, DB2, Postrges, MySQL, SAS, Cognos, Oracle BI Enterprise Edition, SAP Business Objects, or Tableau required
- Master’s Degree or higher degree in operations research, applied statistics, data mining, machine learning, physics or related quantitative discipline required