Data Scientist

Rapid Strategy Washington, DC
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Rapid Strategy is an award-winning, African-American-owned small business providing advanced cybersecurity and IT services to federal and commercial clients. Our mission is to deliver secure, high-impact solutions that support national objectives and operational resilience. We are currently seeking a Data Scientists to support an existing federal customer, contingent upon contract award. This role will support the development, deployment, and continuous improvement of AI/ML and Generative AI solutions in a secure cloud environment. The ideal candidate will combine deep technical expertise in Python, NLP, and statistical modeling with hands-on experience deploying models in production environments. The role includes responsibilities across solution development, model maintenance, and team collaboration.

Responsibilities

·        Research, design, and deploy machine learning and AI models in cloud environments, applying an iterative, hands-on approach.

·        Translate business needs into predictive and prescriptive models that deliver tangible value across the enterprise.

·        Implement and fine-tune models using frameworks such as Transformer, Scikit-learn, SpaCy, XGBoost, and deep learning libraries.

·        Lead the development of new Generative AI capabilities using technologies like GPT, Claude, or HuggingFace platforms.

·        Collaborate with cross-functional teams to understand unique business challenges and apply AI/ML to solve them.

·        Utilize AWS data and AI services to develop scalable data pipelines and model training infrastructure.

·        Maintain a detailed understanding of existing AI/ML models, including data pipelines, training methodology, infrastructure, and monitoring tools.

·        Establish processes to monitor, retrain, and improve model accuracy and performance over time.

·        Expand and improve training datasets while validating input data quality.

·        Present insights and model outputs to technical and non-technical stakeholders, supporting informed decision-making.

·        Collaborate with data engineers, analysts, and business teams to align models with operational objectives.

 Required Qualifications

·        6 to 10 years of hands-on experience developing, deploying, and maintaining AI/ML solutions in professional or academic environments.

·        Bachelor’s degree in Computer Science, Data Science, or a related technology field (Master’s preferred).

·        Advanced proficiency in Python and experience with additional programming languages (e.g., R, Java).

·        Deep knowledge of statistical modeling, machine learning, and data analysis techniques.

·        Experience deploying AI/ML models in AWS environments, using services like S3, SageMaker, Lambda, or DynamoDB.

·        Proficiency with Natural Language Processing (NLP) techniques (e.g., Text Normalization, NER, POS tagging, word embeddings).

·        Familiarity with Generative AI tools and concepts, including prompt engineering and fine-tuning large language models (LLMs).

·        Strong analytical and problem-solving skills, with the ability to work independently and collaboratively.

Preferred Qualifications

·        Experience with cloud-native DevSecOps, CI/CD pipelines for ML models, and model version control.

·        Knowledge of federal data governance, privacy, and compliance requirements (e.g., FedRAMP, FISMA).

·        Prior experience supporting federal agencies or regulated industries in data science or AI strategy roles.

·        Contributions to open-source AI/ML projects or published research.

 

Who Should Apply

This role is ideal for experienced data scientists, ML engineers, and AI technologists passionate about building and maintaining impactful AI/ML products. If you bring strong technical depth, a clear understanding of operational AI, and an eagerness to innovate with generative AI and NLP in secure environments, we encourage you to apply. This is a high-impact opportunity for professionals seeking mission-driven work in a collaborative and forward-looking setting.

 

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