Required Qualifications
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3+ years of experience in applied data science or machine learning roles, including hands-on work with Python, NLP, and LLM implementation.
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3+ years of experience in data exploration, cleaning, analysis, visualization, or data mining.
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Experience working with production-grade systems, data lake environments, and streaming data technologies such as Kafka.
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Proven ability to build and deploy end-to-end ML workflows—from data preparation through model deployment and evaluation.
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Strong aptitude for learning new infrastructure or systems concepts, including pipeline integrations with data lake architectures.
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Demonstrated capability in designing, implementing, and iterating on ML models for document classification, information extraction, summarization, and search.
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Ability to own and maintain data science workflows that support a production system processing millions of documents weekly.
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Active TS/SCI clearance.
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Bachelor’s degree.
Additional Qualifications
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Experience collaborating closely with MLOps and infrastructure teams to deliver reliable model deployment, monitoring, and retraining pipelines.
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Experience supporting platform components such as document indexing, search systems, GPU workloads, or distributed storage environments (e.g., Cloudera).
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Background in developing algorithms using R, Python, SQL, or NoSQL technologies.
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Familiarity with distributed data and computing tools, such as MapReduce, Hadoop, Hive, EMR, Spark, Gurobi, or MySQL.
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Experience with data visualization libraries such as Plotly, Seaborn, or ggplot2.
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Security+ certification.