Description
- Leads data science efforts, working closely with clients and data to understand mission and data.
- Develops algorithms leveraging modern data science technologies including reinforcement learning systems, machine learning and more.
- Ability to analyze and profile varied related and unrelated data sets.
- Provides clients with technical expertise for a project or program.
- Manage technology projects or lead technology solutioning.
- Provides highly technical and specialized guidance and solutions to complex IT problems; performs elaborate analyses and studies.
- Evaluates, recommends, and executes new technologies and updates existing infrastructure to ensure optimal performance and efficiency.
- Develops IT strategies to ensure the systems meet existing and future requirements based on needs and regulations.
- Works in a variety of environments and has excellent verbal and non-verbal communication skills.
Requirements
- Must be a U.S. Citizen to obtain a VA security clearance?
- At least 5 years of experience developing in languages commonly used for data analysis such as Python, R, Julia, or SAS
- Experience working with multiple database types such as SQL, Redis and MongoDB
- Experience configuring and working in cloud-native environments to execute data science activities including data preparation, analysis, and model development, training, and deployment.
- Experience building and integrating the at the application and database level
- Experience developing REST/SOAP APIs and messaging protocols and formats
- At least 2 years of theoretical and practical background in statistical analysis, machine learning, predictive modeling, and/or optimization
- Experience implementing event/data streaming services such as Kafka
- Familiarity with KubeFlow and/or MLOps
- Experience prototyping front-end visualizations utilizing data visualization suites such as Kibana or Splunk
- At least 2 years of experience developing Reinforcement learning systems utilizing at least one of the following methodologies. Finite Markov Decision Processes, Support Vector Machines, Q-Learning, Stochastic Finite State Machines, MCTS or other hybrid Deep Reinforcement Learning processes
- Experience in theoretical and practical background in statistical analysis, machine learning, predictive modeling, and/or optimization.
- Experience developing in languages commonly used for data analysis such as Python, R, Julia, or SAS
- Experience working with databases such as SQL or MongoDB.
- Experience working with large-scale data sets.
- Experience producing data visualizations for a variety of different audiences.
- Excellent verbal and written communications skills along with the ability to present technical data and approaches to both technical and non-technical audiences.
- Ability to work efficiently with a geographically distributed team using virtual collaboration tools