Data Scientist - Remote

Overview

We are looking for a visionary and innovative Data Scientist to join our engineering team. The ideal candidate will have a deep understanding of machine learning, advanced data science techniques and artificial intelligence, combined with hands-on experience developing scalable DL/ML solutions. This is a unique opportunity to contribute to the design and deployment of AI-driven products that have a significant business impact, while working with state-of-the-art tools and technologies.

In Short

• Design, develop, and implement data science models and DL/ML solutions to solve business challenges.
• Analyze large, complex datasets to extract insights and build predictive and prescriptive models.
• Train, test, optimize, and deploy machine learning models in production environments.
• Monitor model performance and conduct regular updates to ensure accuracy and relevance.
• Collaborate with cross-functional teams, including data engineers, software developers, and business stakeholders, to implement AI use cases and integrate solutions into existing workflows and platforms.
• Stay up-to-date with advancements in data science, AI/ML, and cloud technologies to continuously improve our solutions.
• Follow best practices in data security and compliance, especially when working with sensitive data.

Requirements

• Bachelor’s or master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 5+ years of hands-on experience as a Data Scientist, preferably in a cloud-based environment.
• Strong proficiency in relevant programming languages e.g. Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.) or R.
• Proficiency with cloud services, preferably AWS.
• Experience with machine learning frameworks and tools, such as TensorFlow, PyTorch, or XGBoost.
• Experience with deploying and scaling AI/ML models in cloud-based architectures.
• Expertise in working with structured and unstructured datasets, including preprocessing, feature engineering, and model selection.
• Strong analytical, problem-solving, and critical-thinking skills.
• Excellent communication and presentation skills, with the ability to convey technical concepts to non-technical audiences and across different cultures.

Benefits

• Specialized expertise in one or more AI domains, such as NLP, computer vision, or generative AI (e.g., GPT models).
• Experience with MLOps tools and practices, such as MLflow, Docker, and CI/CD pipelines for machine learning.
• Knowledge of deep learning techniques and architectures, such as transformers and convolutional neural networks (CNNs).
• Familiarity with graph databases, knowledge graphs, or graph-based AI approaches.