Description
- Leads data science efforts, working closely with clients, data, developers to understand mission and data.
- Creates data pipelines and flows to clean and transform data for use in data models and applications.
- Develops algorithms leveraging modern data science technologies including reinforcement learning systems, machine learning and more.
- Deep expertise in causal inference, high-dimensional regression, time-series analysis, and forecasting
- Strong proficiency in Python or R (PyData stack: Pandas, NumPy, SciPy, Statsmodels, Scikit-learn)
- Demonstrated ability to communicate statistical findings clearly to non-technical executive audiences
- Lead development of advanced AI/ML systems using techniques such as deep learning, representation learning, time-series modeling, survival analysis, and probabilistic modeling to solve complex healthcare problems or analyze fraud
- Ability to analyze and profile varied related and unrelated data sets.
- 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 security clearance.
- BS with 10+ years of experience in machine learning, artificial intelligence, or applied data science with 7+ years of designing and deploying production machine learning systems.
- 8+ years of experience in Python-based machine learning development using frameworks such as PyTorch, TensorFlow, or equivalent along with solid SQL skills.
- 5+ years of experience deploying production ML systems including model serving, monitoring, ML lifecycle management, and collaboration with engineering teams.
- Experience configuring and working in Azure 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.
- At least 2 years of theoretical and practical background in statistical analysis, machine learning, predictive modeling, and/or optimization.
- 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.