Data Scientist

Intersources Inc.

United States

24 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www2.jobdiva.com

Tech stack

LangGraph Framework Artificial Intelligence Amazon Web Services Microsoft Azure Data as a Services Python (Programming Language) Machine Learning Natural Language Processing OpenAI Tensorflow Azure Machine Learning SQL Databases

+24 more

Feature Engineering Graph Neural Networks Pytorch LangChain Prophet Retrieval-Augmented Generation Large Language Models Snowflake Model Validation Agentic-AI AI Platforms Pyspark LangSmith Scikit Learn Kubernetes Information Technology Optimization Algorithms HuggingFace Xgboost AutoGen Machine Learning Operations Google Gemini KServe Unsupervised Learning

Requirements

Experience with LangChain, LangGraph, AutoGen, Hugging Face, OpenAI, Gemini, or comparable AI frameworks.
Experience with ML observability tools such as Weights & Biases, LangSmith, TensorBoard, or similar platforms.
Knowledge of computer vision, graph neural networks, adversarial machine learning, and optimization techniques.
Experience with Snowflake, PySpark, OneLake, SageMaker, Azure AI Services, and data visualization platforms.
Professional experience in data science, machine learning, or AI engineering.
Advanced proficiency in Python and SQL.
Strong experience with PyTorch, TensorFlow, Scikit-learn, XGBoost, or comparable frameworks.
Hands-on experience with statistical modeling, supervised and unsupervised learning, feature engineering, and model evaluation.
Experience building production solutions using LLMs, NLP, RAG, embeddings, fine-tuning, and agentic AI frameworks.
Experience with time-series forecasting methods such as Prophet, ARIMA, SARIMA, and gradient-boosting models.
Experience deploying scalable ML pipelines using Kubernetes, Kubeflow, KServe, or equivalent technologies.
Working knowledge of cloud AI and data services across Azure, AWS, or GCP.
Experience with relational, analytical, graph, and vector databases.
Strong problem-solving, stakeholder engagement, communication, and cross-functional collaboration skills.
Master’s degree in Data Science, Computer Science, Engineering, Statistics, Operations Research, or a related quantitative discipline.

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