The ad says“Experience with Go programming language”
The ad says“Strong proficiency in Python programming language”
Values
The ad says“Knowledge of cloud platforms (e.g., AWS, GCP, Azure)”
The ad says“Knowledge of cloud platforms (e.g., AWS, GCP, Azure)”
The ad says“Knowledge of cloud platforms (e.g., AWS, GCP, Azure)”
The ad says“Experience with natural language processing (NLP) and large language models (LLMs) is a plus”
The ad says“Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib, Seaborn)”
The ad says“Experience with data version control and MLOps practices”
The ad says“Experience with natural language processing (NLP) and large language models (LLMs) is a plus”
The ad says“Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib, Seaborn)”
The ad says“Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib, Seaborn)”
The ad says“Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)”
The ad says“Acceleration frameworks such as RAPIDS, Spark is preferable”
The ad says“Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib, Seaborn)”
The ad says“Acceleration frameworks such as RAPIDS, Spark is preferable”
The ad says“Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)”
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 Networking, Security and Language use-cases.
You will work closely with cross-functional teams to uncover insights, solve complex problems, and drive data-informed decision-making
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 machine learning models, including traditional algorithms like CNNs, XGBoost, Transformers, Various Graph ML algorithms, Time Series modelling, LLM 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
Collaboration: Work effectively with data engineers, analysts, and domain experts to understand business requirements and translate them into actionable data insights