Data Scientist

Gravity IT Resources; Brasil; Remote; BRL 486,000 - 649,000; Full time

Job summary

Gravity IT Resources is seeking a highly technical Senior Data Scientist / Applied AI Engineer to support a range of ML, data engineering, predictive analytics, and Generative AI initiatives in a large enterprise. This is a hands-on, coding-heavy role with ~95% focus on building, modeling, and deploying solutions for business challenges.

The position is remote across Latin America, start ASAP, contract for 6 months with potential extension, and involves collaboration with stakeholders as needed.

Qualifications

• 4+ years of SQL experience with complex joins, CTEs, window functions, and performance tuning.
• 4+ years of Python development experience.
• 2+ years of PySpark or distributed data processing.
• Experience building data pipelines and integrating multiple data sources.

Responsibilities

• Develop, optimize, and maintain complex SQL queries involving joins, CTEs, window functions, and query performance tuning.
• Build data pipelines using Python, PySpark, and related data engineering technologies.
• Extract, transform, and integrate data from multiple enterprise sources to support analytics and machine learning initiatives.
• Design scalable data processing workflows and ensure data quality, accuracy, and performance.

Skills

SQL

Python

Data pipelines

ETL

Tools

PySpark

Job description

Senior Data Scientist / Applied AI Engineer

Location: Remote – Latin America (LATAM)

Job Type: Contract (6 Months Initially, Potential for Extension)

Start Date: ASAP

Position Overview

Our client is seeking a highly technical, hands on Senior Data Scientist / Applied AI Engineer to support a variety of machine learning, data engineering, predictive analytics, and Generative AI initiatives within a large enterprise environment. This role will operate as a versatile technical problem-solver, developing solutions for a wide range of business challenges rather than supporting one specific product or functional area. The ideal candidate combines strong traditional machine learning and data engineering fundamentals with practical experience building Generative AI applications, leveraging agentic coding tools, and deploying production-ready solutions. This is an extremely hands on engineering role, with approximately 95% of the work focused on coding, building, modeling, engineering, and deploying solutions. The remaining time involves internal collaboration, technical discussions, and occasional stakeholder interaction. Candidates must be comfortable taking ambiguous business problems, identifying appropriate technical approaches, and independently developing working solutions.

Key Responsibilities

Data Engineering & Advanced Analytics

• Develop, optimize, and maintain complex SQL queries involving joins, CTEs, window functions, and query performance tuning.
• Build and manage data pipelines using Python, PySpark, and related data engineering technologies.
• Extract, transform, and integrate data from multiple enterprise sources to support analytics and machine learning initiatives.
• Design scalable data processing workflows and ensure data quality, accuracy, and performance.
• Work independently to troubleshoot data engineering challenges and develop efficient technical solutions.

Machine Learning & Predictive Modeling

• Design, develop, and deploy traditional machine learning models supporting forecasting, classification, regression, and predictive analytics.
• Build models for business use cases such as demand forecasting, lead scoring, customer propensity, and other predictive applications.
• Apply machine learning frameworks including XGBoost and LightGBM to develop production-ready models.
• Utilize SHAP and other explainability techniques to interpret model predictions, debug model behavior, and communicate findings to nontechnical stakeholders.
• Evaluate model performance, optimize predictive accuracy, and identify opportunities for continuous improvement.
• Translate business requirements into practical machine learning solutions with measurable outcomes.

Generative AI & Agentic Development

• Design, build, and deploy Generative AI applications using modern LLM technologies.
• Develop Retrieval-Augmented Generation (RAG) solutions, vector search capabilities, and AI agents with tool-use functionality.
• Integrate LLM APIs into applications, automation workflows, and enterprise solutions.
• Build scalable asynchronous or batch-processing workflows capable of handling thousands of LLM API calls.
• Apply LLMs to practical use cases such as data labeling, metadata extraction, text processing, and workflow automation.
• Leverage agentic coding platforms such as Claude Code, OpenAI Codex, Cursor, or comparable tools as part of daily software development.
• Use AI-assisted development workflows to accelerate coding, debugging, testing, and solution delivery.

Cloud Engineering & Solution Deployment

• Develop and deploy machine learning, data science, and AI applications using cloud-based platforms.
• Work within Microsoft Azure, Databricks, or comparable cloud environments, including AWS and Google Cloud.
• Build and support containerized applications and cloud-based services.
• Move solutions beyond notebooks and prototypes into functional applications, APIs, or production environments.
• Collaborate with technical teams to ensure solutions are scalable, reliable, and maintainable.
• Support end-to-end technical implementation, troubleshooting, and ongoing optimization.

Rapid Prototyping & Technical Problem-Solving

• Develop proofs of concept and working prototypes to validate new business use cases.
• Translate loosely defined business problems into executable technical solutions.
• Work across multiple concurrent initiatives involving data engineering, predictive modeling, AI, and automation.
• Evaluate technical approaches and select appropriate tools, frameworks, and architectures.
• Communicate technical questions, blockers, and recommendations clearly to team leadership.
• Independently manage technical deliverables in a fast-moving environment with limited oversight.

Required Qualifications

Data Engineering & Programming

• 4+ years of strong SQL experience, including complex joins, CTEs, window functions, query optimization, and performance tuning.
• 4+ years of hands-on Python development experience.
• 2+ years of experience with PySpark or comparable distributed data processing technologies.
• Strong experience building data pipelines, manipulating large datasets, and integrating multiple data sources.

Traditional Machine Learning

• Demonstrated hands-on experience developing and deploying forecasting and classification models for real-world business applications.
• Experience with predictive modeling use cases such as demand forecasting, lead scoring, customer propensity, or similar applications.
• Production experience with XGBoost and/or LightGBM.
• Hands-on experience using SHAP to explain, interpret, and troubleshoot machine learning models.
• Strong understanding of regression, classification, forecasting, feature engineering, model evaluation, and predictive analytics.
• Machine learning experience must come from production environments or substantial real-world projects; coursework alone is insufficient.

Generative AI & Agentic Coding

• 6+ months of daily, hands-on agentic coding experience using Claude Code, Codex, Cursor, or comparable development agents.
• Demonstrated experience personally building Generative AI products or applications, including:
• Retrieval-Augmented Generation (RAG)
• Vector search
• AI agents with tool-use capabilities
• Experience integrating LLM APIs into scalable applications or automated workflows.
• Experience executing large-scale LLM API workloads, including asynchronous or batch-processing jobs involving thousands of API calls.
• Must be an active technical builder with practical software development experience, rather than someone whose background is limited to AI strategy, advisory work, or prompt experimentation.

Cloud & Deployment

• Hands-on experience with Azure and/or Databricks, with experience in both strongly preferred.
• Strong experience with Google Cloud / Vertex AI or AWS / SageMaker is also acceptable.
• Ability to develop, package, and deploy working machine learning, data engineering, or AI solutions.
• Familiarity with cloud infrastructure, application deployment, and production environments.

Preferred Qualifications

• Experience developing recommendation engines or personalization systems.
• Experience building web applications using agentic coding tools.
• Experience developing APIs with FastAPI or comparable Python frameworks.
• Experience with Docker, containerized applications, and end-to-end deployment.
• Familiarity with Kubernetes, Azure Kubernetes Service (AKS), or comparable orchestration platforms.
• Front-end development experience with React and/or Next.js.
• Experience developing production applications that combine data engineering, machine learning, Generative AI, and user-facing interfaces.
• Experience supporting multiple business functions and rapidly adapting to unfamiliar technical challenges.