At Two Six Technologies, we build, deploy, and implement innovative products that solve the world’s most complex challenges today. Through unrivaled collaboration and unwavering trust, we push the boundaries of what’s possible to empower our team and support our customers in building a safer global future.
Two Six Technologies is looking for a full time Full Stack Data Scientist to join our Data Science (DS) team at Two Six in the Information Advantage business unit. At Two Six Technologies, we build, deploy, and implement innovative products that solve the world’s most complex challenges today. Through unrivaled collaboration and unwavering trust, we push the boundaries of what’s possible to empower our team and support our customers in building a safer global future.
Through private research and development, relentless innovation, and deep technical expertise in cyber, information operations, data science, electronic systems, mobility, and user experience, we serve customers that include DARPA, the Department of State, U.S. Cyber Command, the Department of Homeland Security, and beyond.
Overview
We’re looking for a Full Stack Data Scientist that will help our customers discover the information hidden in vast amounts of data to sense, make sense, and act in the Information Environment. In this role, you will make sure AI/ML projects are more systematic, repeatable, and well maintained. This is a hybrid role where you will have a data science focus and an MLOPs engineering focus. We value that our DS team is able to act and add value at their own pace, while ensuring that our impactful work is packaged so that it can be easily integrated into our products when both the data science and the product are ready. We feel the best way to accomplish this is by having a data scientist with MLOPs skills sitting on the DS team. The DS responsibilities center around using AI/ML to build solutions to challenging problems. The goal of your MLOPs role will be to help us deploy models and ad-hoc tools for immediate use by internal users and ensure they are deployed in a way that allows them to be easily inserted into the company's main products. Additionally, we will utilize your MLOps skills to monitor algorithms metrics, improve performance, and set best practice.
MLOps Engineer Responsibilities
- You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.
- Oversee Data and Analytics system architecture, scalability, reliability, and performance.
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- Develop and maintain internal ML Ops tooling and AWS-based infrastructure.
- Collaborate with ML Scientists to design, deploy, and optimize ML solutions in the cloud.
- Collaborate with Engineers and Product to ensure smooth integration of ML models into our product development pipeline.
- Optimize and automate processes to enhance the efficiency of ML model building, evaluation, and deployment.
- Apply conceptual understanding of relational data stores, big data environments, APIs, unstructured data, and process flows to support data science infusion into new and existing products.
- Construct optimized data pipelines to feed ML models
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
- Work with our subject matter experts and engineers to turn prototypes into scalable, production software
Data Science Responsibilities
- Build prototype tools to help our SMEs extract insights from our data
- Deliver insights on existing initiatives and come up with innovative product growth opportunities
- Explore and extract value from out database of over 100 million documents
- Use ML to perform various classification tasks at the document and author level
- Use Neural Networks and other techniques to leverage our unlabeled data
- Design and execute experiments to evaluate the integrity of our data sampling and data collection strategies.
Additionally, as a part of the Data Science team you’ll have opportunities to work on projects that expand your skills, learn from peer mentors, and iterate internal research and development. You may experiment with a range of data science techniques to build tools and produce insight, including machine learning, network analysis, sampling methodologies, multi-language NLP, and data visualization.
- You’ll work with our customers and internal experts to understand their problems and creatively identify and implement solutions in the form of models, tools, or one-off analytics.
- You’ll be responsible for projects that span statistical and mathematical reasoning, business communications and leadership, and computer programming
- You’ll leverage state of the art open source techniques and tools like LLMs to deliver value across our customer-facing and internal product suite, including quick turn notebooks or applications for analyst use.
- You’ll work with our Product team to advance the product roadmap and bring ideas and new field developments to expand our market advantage.
- You’ll design and execute experiments to evaluate the integrity of our data sampling and collection strategies as well as the accuracy and utility of our models.
Here are some of the skills you’ll need to dive into the role:
- Technical
- You have mid-level career experience. For example, you have 2+ years experience in a MLOPs, ML Engineer, or Data Science role (or 1+ with a PhD), including 2+ years experience with Python programming language (Intermediate-level or higher) and least 1 year of experience productionizing, monitoring, and maintaining models
- Previous ML Ops or process automation experience and experience with common Machine Learning libraries (Pandas, PyTorch, scikit-learn, etc.)
- Ability to design and implement robust APIs and architect containerization solutions to streamline application builds and deployments
- Collaboration
- Ability to understand the needs of data science, product, customer support, and engineering teams
- Reach a consensus about goals and methods for completing projects or tasks
- Recognize others’ contributions, giving credit where it’s due
- Actively listen to identify obstacles and address problems cooperatively
- Flexibility
- Adapting successfully to changing situations and environments
- Ability to adapt to multiple long and short project timelines
- Persisting in the face of unexpected difficulties
‘Nice to have’ expertise to highlight in your resume
- Bachelor’s degree in Physics, Computer Science, Math, Statistics, Economics, Engineering, or similar field.
- Significant experience working in and leveraging the services of a cloud computing platform, e.g. AWS (preferred)
- Familiarity with software development procedures/tools like Agile, Git, and Jira
Clearance/Citizenship
- Eligibility to obtain a government clearance if required/needed.
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