AI Data Scientist

Expert Technical Solutions; EE. UU.; A distancia; USD 90.000 - 140.000; Jornada completa

Descripción de la vacante

Expert Technical Solutions has an immediate opening for a Data Scientist with a premier client in the financial services sector. This 6-month contract with extension potential is fully remote from home.

You will design, build, and operationalize intelligent data systems powering ML and AI-driven product capabilities. The Data Scientist partners with Product, BI, and Data Engineering teams to establish data science standards, architect scalable AI systems, and deliver measurable business impact

Formación

• Minimum of 3-5 years of designing, building, and operationalizing end-to-end data, ML, NLP and AI systems in production environments.
• Sound knowledge of machine learning lifecycle from data gathering and data preparation to model deployment, monitoring and iteration.
• Strong programming skills in Python & SQL; production-quality code experience.
• Hands-on experience with LLM-based systems (e.g., RAG architectures, prompt engineering, model evaluation, API integration).
• Exposure to cloud AI/ML services and modern data platforms (Databricks, AWS Bedrock, AWS SageMaker).
• Experience integrating external AI/LLM APIs and building operational AI services.
• Strong software engineering fundamentals including CI/CD, testing practices, and containerization.
• Strong analytical and problem-solving skills; translate business requirements into AI/data solutions.

Responsabilidades

• Deliver actionable, data-driven insights and reporting to inform business decisions and evaluate performance across products, operations, and AI systems.
• Design and maintain scalable data-to-AI pipelines covering ingestion, transformation, feature/prompt engineering, model training, orchestration, deployment, and monitoring.
• Deliver AI-driven solutions that measurably improve key product or operational metrics (e.g., revenue uplift, cost reduction, customer satisfaction, latency).
• Partner with Data and Product to identify and execute AI opportunities aligned with strategic objectives.
• Establish and enforce AI and machine learning governance and best practices across the organization.
• Build reliable experimentation frameworks to validate model performance and business impact and drive iterative improvements.
• Own the scalability, robustness, observability, optimization and operational excellence of production AI systems and data pipelines.
• Develop scalable data pipelines, data models and feature engineering workflows; implement ML Ops practices including CI/CD, model versioning, monitoring, and retraining.
• Design model-serving APIs and ensure system reliability and performance.
• Transition workloads from on-premise to AWS/Azure environments as needed.
• Design and analyze A/B tests and experiments to validate model and product performance.
• Provide business insights derived from data to guide product and strategic decisions.

Conocimientos

Python

SQL

ML lifecycle

Data analysis

Problem solving

LLM integration

Herramientas

Databricks

AWS Bedrock

SageMaker

CI/CD

Docker

LLM APIs

Descripción del empleo

Expert Technical Solutions has an immediate opening for a Data Scientist with one of our premier clients in the financial services industry.

This is 6 months + contract with a strong possibility of extension on conversion to permanent.

The position is fully Remote from home.

You will be responsible for designing, building, and operationalizing intelligent systems that extract value from data and power ML and AI-driven product capabilities.

This role spans data science, applied AI (including generative AI and LLM-based systems), production grade machine learning engineering and advanced analytics.

The Data Scientist partners with Product, BI, and Data Engineering teams to establish data science and machine learning standards, architect scalable AI systems, and deliver measurable business impact across internal and customer-facing solutions.

Responsibilities:

• Deliver actionable, data-driven insights and reporting to inform business decisions and evaluate

performance across products, operations, and AI systems.

• Design and maintain scalable data-to-AI pipelines covering ingestion, transformation, feature/prompt engineering, model training, orchestration, deployment, and monitoring.
• Deliver AI-driven solutions that measurably improve key product or operational metrics (e.g., revenue uplift, cost reduction, consumer satisfaction, platform efficiency, prediction accuracy, latency reduction).
• Partner with Data and Product to identify and execute AI opportunities aligned with strategic objectives.
• Establish and enforce AI and machine learning and data operational standards, governance, and best practices across the organization.
• Build reliable experimentation frameworks to validate model performance and business impact and drive iterative improvements through reliable model evaluation and testing.
• Own the scalability, robustness, observability, optimization and operational excellence of production AI systems and data pipelines.
• Perform exploratory data analysis and develop statistical and machine learning models (e.g., regression, clustering, classification) to address business problems.
• Develop, fine-tune, and optimize machine learning and generative AI models including prompt engineering strategies and structured evaluation frameworks for LLM-based systems to ensure performance, scalability, and reliability.
• Design and build AI-powered product features, including predictive models, optimization systems, and LLM based applications (e.g., RAG systems, AI assistants, document intelligence).
• Research and apply emerging AI techniques to improve product capabilities and operational efficiency.
• Develop scalable data pipelines, data models and feature engineering workflows.
• Implement ML Ops practices including CI/CD, model versioning, monitoring, and automated retraining.
• Design model-serving APIs and ensure system reliability and performance.
• Transition workloads from on-premise infrastructure to AWS and/or Azure environments as needed.
• Design and analyze A/B tests and experiments to validate model and product performance.
• Provide business insights derived from data to guide product and strategic decisions.
• Own technical design of AI solutions in collaboration with Product, Engineering, BI, and Data teams, translating business requirements into scalable system architectures.
• Translate ambiguous business problems into well-defined data and AI solutions with clear success metrics and implementation plans.

Experience:

• Minimum of 3-5 years of designing, building, and operationalizing end-to-end data, ML, NLP and AI systems in production environments
• Sound knowledge of machine learning lifecycle from data gathering and data preparation to model deployment, monitoring and iteration.
• Strong programming skills in Python & SQL, experience writing production-quality code
• Hands-on experience with LLM-based systems (e.g., RAG architectures, prompt engineering, model evaluation, API integration).
• Exposure to Databricks, AWS Bedrock, AWS Sagemaker, Cloud based AI/ML services and modern data platforms
• Experience integrating external AI/LLM APIs and building and operationalizing application-layer AI services is desirable.
• Strong software engineering fundamentals, including experience with CI/CD, testing practices, and containerization concepts.
• Strong analytical and problem-solving skills, with the ability to translate ambiguous business requirements into structured AI and data solutions.