Senior Data Scientist (6 months contract)
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Key skills for this role
About the Role
Contango seeks a Senior Data Scientist for a 6-month contract in Abu Dhabi to design and deploy end-to-end Data & AI solutions. The role involves framing use cases, building data pipelines, developing production ML systems, and ensuring system reliability.
Key Skills for This Role
Responsibilities
- Translate client business problems into end to end system architectures combining Data, ML, and software components
- Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability
- Develop ML solutions using production quality code (Python/JS), following software engineering best practices
- Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment
- Containerize and deploy services using Docker, Kubernetes, and cloud native tooling
- Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance
- Design systems for high availability, fault tolerance, and horizontal scalability
- Produce developer focused documentation (APIs, architecture diagrams, runbooks)
Requirements
- 5+ years of experience in data science or a related analytical field
- 5+ years delivering end to end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production
- Bachelor's degree (Master's preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering)
- Proficiency in Python (pandas/NumPy) and SQL
- Experience applying software engineering methodologies and best practices
- Prior experience at management consulting firms and/or Big Tech is an advantage
- Client serving experience is an advantage
Full Job Posting
Role Overview
- As a Senior Data Scientist, you will independently work on specific data projects and be responsible for implementing analytical solutions. You will design, build, deploy, and support end to end Data & AI solutions.
Key Responsibilities
- Translate client business problems into end to end system architectures that combine Data, ML, and software components.
- Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.
- Make explicit trade offs across performance, cost, latency, and maintainability.
- Define success metrics, SLAs, and non functional requirements (reliability, security, scalability).
- Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.
- Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.
- Collaborate with platform teams to define data models, schemas, and storage strategies.
- Enforce standards for data validation, testing, and monitoring within production systems.
- Develop ML solutions using production quality code (Python/JS), following software engineering best practices.
- Structure codebases into maintainable, testable modules, with clear separation of concerns.
- Implement unit, integration, and end to end tests for data and ML components.
- Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.
Required Experience and Qualifications
- Prior experience at management consulting firms and/or Big Tech is an advantage.
- Client serving experience is an advantage.
- 5+ years of experience in data science or a related analytical field.
- 5+ years delivering end to end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production.
- Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security.
- Prior experience in developing AI solutions on public cloud services is an advantage.
- Bachelor’s degree (Master’s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering).
Technical Expertise
- Coding & data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.
- Statistics & experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.
- ML modelling: Feature engineering, model selection, cross validation, metrics, and hyperparameter tuning (supervised/unsupervised).
- Data prep & analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.
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