Sr. Data Scientist (Network Security Domain)

We are seeking a talented and motivated Data Scientist to join our Machine Learning team. In this role, you will contribute to a wide range of data-driven projects, from exploratory data analysis to building and deploying machine learning models for a variety of Security and Networking use-cases. You will work closely with cross-functional teams to uncover insights, solve complex problems, and drive data-informed decision-making.

Responsibilities

Data Analysis and Visualization: Conduct exploratory data analysis (EDA) to understand data characteristics and identify patterns. Utilize data visualization techniques to communicate insights effectively.

Data Preparation and Engineering: Ingest, clean, preprocess, and transform data to prepare it for analysis and modeling. Handle missing values, outliers, and inconsistencies. Labeling datasets appropriately for classification problems.

Machine Learning: Build, train, and evaluate custom machine learning models for security use-cases such as threat/vulnerability detection, including traditional algorithms like CNNs, LSTM, Boosted Decision Trees, DNNs as well as newer NLP/Vision models using Transformers, Mamba etc.

Model Deployment: Collaborate with engineering teams to deploy models into production, ensuring scalability and reliability. You need to be aware of and have experience in model quantization, model evaluation and various deployment formats like ONNX, GGUF etc.

Problem-Solving: Apply critical thinking and problem-solving skills to tackle complex data challenges, to continually improve training pipelines and optimize inference.

Collaboration: Work effectively with data engineers, analysts, and domain experts to understand business requirements and translate them into actionable data insights.

Qualifications:

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 (e.g., Pandas/Polars, NumPy, Matplotlib, Seaborn).

Knowledge of machine learning algorithms and techniques.

Familiarity with deep learning frameworks (e.g., 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.

Preferred Qualifications:

Experience with Go/Rust programming language.

Experience working with threat modeling and detection, anomaly detection or related fields is a bonus.

Distributed training frameworks such as RAPIDS, Dask, Ray is preferable.

Knowledge of cloud platforms (e.g., AWS, GCP, Azure).

Experience with data version control and MLOps frameworks such as MLFlow/ClearML.

Familiarity with cloud-native DevOps such Docker, K8s, Helm, Terraform.