Data Scientist

Overview

We seek driven and passionate Data Scientists motivated by solving intricate business challenges through Artificial Intelligence (AI) and Machine Learning (ML). This position requires designing, developing, and delivering scalable AI/ML solutions that produce substantial business value.

Core Responsibilities

• Create and implement end-to-end machine learning systems.
• Develop and refine NLP and deep learning models suitable for production environments.
• Analyze extensive and diverse datasets, both structured and unstructured, to uncover actionable business insights.
• Convert complex business problems into scalable AI and ML solutions.
• Collaborate with engineering teams to deploy, monitor, and continuously enhance ML models.
• Conduct experiments on model performance, including hyperparameter tuning and evaluation.
• Establish strategies for model monitoring, retraining, and optimization.
• Effectively communicate technical results and insights to various stakeholders with differing levels of technical knowledge.
• Provide mentorship to junior data scientists and promote best practices across teams.

Essential Qualifications

• Bachelor's or Master's in Computer Science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or related disciplines.
• Between 3 to 9 years of practical experience in Data Science, Machine Learning, Applied AI, or Advanced Analytics.
• In-depth knowledge of machine learning algorithms, statistics, probability, and optimization methods.
• Proficiency in Python preferred; Scala experience is also relevant.
• Experienced in model evaluation approaches, experimentation, and A/B testing techniques.
• Strong analytical and problem-solving abilities.
• Excellent communication skills and the ability to effectively manage stakeholder relationships.

Desirable Skills

• Experience working with Natural Language Processing (NLP), Large Language Models (LLMs), or Generative AI technologies.
• Hands-on familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Understanding of text mining, embeddings, vector databases, and transformer architectures.
• Experience deploying machine learning models in cloud or production settings.
• Knowledge of MLOps concepts including model monitoring, versioning, and continuous integration/delivery (CI/CD) pipelines.
• Familiarity with methods for model explainability and interpretability.