Data Scientist- AI- Remote

As a Data Scientist for this remote opportunity, you will be responsible for designing and implementing end-to-end ML solutions across structured data, text, and image modalities. Your role will involve applying rigorous statistical thinking, including experimental design and A/B testing, to validate hypotheses. Additionally, you will be expected to build and fine-tune LLMs for domain-specific applications, develop computer vision pipelines, evaluate and integrate foundation models, and oversee model performance from training to production. You will also have the opportunity to mentor junior data scientists and contribute to internal tooling and best practices. Core Responsibilities: - Design and implement end-to-end ML solutions - Apply rigorous statistical thinking for hypothesis validation - Build and fine-tune LLMs for domain-specific applications - Develop computer vision pipelines for various tasks - Evaluate, select, and integrate foundation models - Own model performance from training to production - Mentor junior data scientists and contribute to internal best practices Qualification Required: - Strong grounding in probability theory and distributions - Practical experience with gradient boosting, regression, and SVMs - Ability to design statistically sound experiments - Familiarity with Bayesian frameworks for uncertainty quantification - Knowledge of Bayesian inference, probabilistic modeling, ensemble methods, and hypothesis testing - Hands-on experience with LLMs and generative AI - Ability to design and evaluate retrieval-augmented generation pipelines - Familiarity with model evaluation frameworks - Understanding of model quantization and inference cost optimization - Experience with fine-tuning open-source LLMs and evaluating retrieval-augmented generation pipelines - Experience with detection and segmentation frameworks - Proficiency with vision transformer architectures - Ability to handle real-world CV challenges - Familiarity with multimodal models for vision-language tasks For any further details, kindly refer to the provided email address. As a Data Scientist for this remote opportunity, you will be responsible for designing and implementing end-to-end ML solutions across structured data, text, and image modalities. Your role will involve applying rigorous statistical thinking, including experimental design and A/B testing, to validate hypotheses. Additionally, you will be expected to build and fine-tune LLMs for domain-specific applications, develop computer vision pipelines, evaluate and integrate foundation models, and oversee model performance from training to production. You will also have the opportunity to mentor junior data scientists and contribute to internal tooling and best practices. Core Responsibilities: - Design and implement end-to-end ML solutions - Apply rigorous statistical thinking for hypothesis validation - Build and fine-tune LLMs for domain-specific applications - Develop computer vision pipelines for various tasks - Evaluate, select, and integrate foundation models - Own model performance from training to production - Mentor junior data scientists and contribute to internal best practices Qualification Required: - Strong grounding in probability theory and distributions - Practical experience with gradient boosting, regression, and SVMs - Ability to design statistically sound experiments - Familiarity with Bayesian frameworks for uncertainty quantification - Knowledge of Bayesian inference, probabilistic modeling, ensemble methods, and hypothesis testing - Hands-on experience with LLMs and generative AI - Ability to design and evaluate retrieval-augmented generation pipelines - Familiarity with model evaluation frameworks - Understanding of model quantization and inference cost optimization - Experience with fine-tuning open-source LLMs and evaluating retrieval-augmented generation pipelines - Experience with detection and segmentation frameworks - Proficiency with vision transformer architectures - Ability to handle real-world CV challenges - Familiarity with multimodal models for vision-language tasks For any further details, kindly refer to the provided email address.