Senior Data Scientist: ML, NLP & Production Analytics

Versa Networks; Santa Clara (CA); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

ESOP

Bonuses

Statutory Benefits

Relocation

Flexi Working

Performance Management

Career Progression

Global Mobility

Job summary

Versa Networks in Santa Clara, CA, seeks an experienced Data Scientist to join the Machine Learning team. You will explore data, build and deploy ML models for networking, security and language use-cases, and work with cross-functional teams to drive data-informed decisions.

You will handle data prep, feature engineering, and model deployment with modern frameworks (TensorFlow, PyTorch) and tools, while applying statistical thinking and clear communication.

Qualifications

• Familiarity with deep learning frameworks and Python.
• M Sc in CS/Engineering or related field.
• NL P and LLM experience is a plus.
• Knowledge of ML algorithms and data analysis tools.
• Experience with visualization tools and collaboration skills.
• 8+ years in data science or related field.

Responsibilities

• Perform EDA and visualize data to communicate insights.
• Prepare, clean, and feature engineer data for models.
• Build, train, and evaluate ML models (CNNs, Transformers, Graph ML, time series).
• Deploy models into production with scalable pipelines.
• Collaborate with data engineers, analysts, and domain experts.

Skills

Python

Deep Learning

NLP

Go programming

Data Visualization

Problem-Solving

Communication

Education

Master's degree in Computer Science/Engineering

Tools

TensorFlow

PyTorch

XGBoost

ONNX

GGUF

RAPIDS

Spark

Pandas

NumPy

Matplotlib

Seaborn

AWS

GCP

Azure

Job description

Versa Networks in Santa Clara, CA, seeks an experienced Data Scientist to join the Machine Learning team. You will explore data, build and deploy ML models for networking, security and language use-cases, and work with cross-functional teams to drive data-informed decisions.

You will handle data prep, feature engineering, and model deployment with modern frameworks (TensorFlow, PyTorch) and tools, while applying statistical thinking and clear communication.