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Data Scientist utilizing data to solve global challenges at Booz Allen. Engaging with clients and analyzing complex data through machine learning and AI methodologies.
About the role
- Conduct exploratory data analysis (EDA) to understand data characteristics and identify patterns
- Utilize data visualization techniques to communicate insights effectively
- Ingest, clean, preprocess, and transform data; handle missing values, outliers, and inconsistencies
- Label datasets appropriately for classification problems
- Build, train, and evaluate custom machine learning models for security use-cases (threat/vulnerability detection) using CNNs, LSTM, Boosted Decision Trees, DNNs, Transformers, Mamba, etc.
- Collaborate with engineering teams to deploy models into production, ensuring scalability and reliability
- Apply model quantization, model evaluation, and deploy models in formats like ONNX and GGUF
- Improve training pipelines and optimize inference performance
- Work with data engineers, analysts, and domain experts to translate business requirements into data solutions
Requirements
- Master’s degree in computer science/engineering
- 4-5 years of experience in data science or a related field
- Strong proficiency in Python programming language
- Experience with data analysis and visualization tools (Pandas/Polars, NumPy, Matplotlib, Seaborn)
- Knowledge of machine learning algorithms and techniques
- Familiarity with deep learning frameworks (TensorFlow, PyTorch)
- Experience with natural language processing (NLP) and large language models (LLMs) is a plus
- Strong problem-solving and analytical skills
- Excellent communication and collaboration skills
- Applicants must be authorized to work in the US
- Preferred: Experience with Go or Rust
- Preferred: Experience with threat modeling, detection, or anomaly detection
- Preferred: Distributed training frameworks such as RAPIDS, Dask, Ray
- Preferred: Knowledge of cloud platforms (AWS, GCP, Azure)
- Preferred: Experience with data version control and MLOps frameworks such as MLFlow/ClearML
- Preferred: Familiarity with cloud-native DevOps (Docker, K8s, Helm, Terraform)
Benefits
- Competitive compensation and incentives, including pre-IPO equity
- Comprehensive medical, dental, and vision insurance plans
- Paid Time Off (vacation days, sick leave, and paid holidays)
- Flexible work environment (remote and hybrid options)
- Professional development (training, certifications, and educational resources)
- Employee recognition programs and awards
- Generous parental leave policies
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