Data Scientist (Supply Chain Resilience) - Clearance Required
Overview
LMI: Innovation at the Pace of Need™
At LMI, we’re reimagining the path from insight to outcome at the new speed of possible. Combining a legacy of over 60 years of federal expertise with our innovation ecosystem, we minimize time to value and accelerate mission success. We energize the brightest minds with emerging technologies to inspire creative solutioning and push the boundaries of capability. LMI advances the pace of progress, enabling our customers to thrive while adapting to evolving mission needs.
LMI is seeking a skilled Supply Chain Resilience Consultant to support DoD’s Chief Digital and Artificial Intelligence Office (CDAO). A successful consultant demonstrates competency in client engagement, communications, research, data analysis, and solutioning in support of supply chain resilience concepts and methodologies. Work is primarily located at the LMI Tysons, VA office with remote/telework allowed. Periodic onsite client engagements at classified and unclassified levels could be held at the Suffolk Building in Falls Church, VA.
Responsibilities
- Provides program management support and serves as a key consultant for supply chain resilience, data science, and supply chain risk management (SCRM) activities supporting DOD's risk mangement capabilities.
- Serves as subject matter expert for the integration of risk data from various commercial and government sources into Advana.
- Works collaboratively with federal government organizations, and industry in developing overarching SCRM strategies.
- Utilize analytical tools and techniques to gather, analyze, and interpret data for risk analysis and reporting.
- Works collaboratively with project teams to design outputs of work products.
- Communicates with clients through written reports and oral presentations.
- Designs, builds, and tests UI components for analysis and navigation using best practices in conjunction with user requirements.
- Collaboate with team on new development efforts using a background of Qlik and Tableau knowledge/experience to implement new features, such as user-inputted parameters for data collection and display.
- Works with users to collect feedback and writes requirements for Qlik development.
- Works with data teams to help manage the flow of data from acquisition and ingestion into Advana through extract, transform, and load activities using Databricks to power UI.
- Prioritizes development and works with data and other project stakeholders to architect solutions.
- Foster relationships with external partners and agencies to enhance risk analysis and information sharing.
- Stay updated on the latest advancements in data engineering, data science, and supply chain technologies through industry conferences, training, and research to drive innovation in SCRM.
- Apply database, statistical, machine learning, and natural language processing (NLP) algorithms to analyze large volumes of data, identify patterns, and uncover potential vulnerabilities and threats within the supply chaiin.
Qualifications
- Minimum of 5 years of experience leveraging data analytics or data science approaches. Application of that experience in the area of supply chain management, supply chain risk management (SCRM), intelligence, or related field is preferred.
- Bachelor's degree in data science, mathematics, statistics, economics, computer science, engineering, or a related business or quantitative discipline.
- Strong experience with Qlik Sense, Tableau, SQL, Python, and Advana is highly desired.
- Understanding of basic supply chain concepts to help to align data engineering and data science techniques with specific supply chain risk challenges.
- Excellent communication skills, with the ability to convey technical findings and insights to both technical and non-technical stakeholders.
- Strong analytical thinking and problem-solving skills.
- Continuous learning mindset and a passion for exploring new techniques to enhance SCRM capabilities.
- Familiarity with SCRM tools and SCRM regulations and frameworks preferred.
- Active Secret clearance. Top Secret preferred.