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Contango

Contango is a strategic partner for transformative growth and sustained success in the dynamic Abu Dhabi market. As a trusted advisor to ADQ's portfolio companies, Contango empowers CEOs to drive strategic growth initiatives, navigate disruptive forces, and maximize long-term value creation. The locally based team of industry experts excels in providing comprehensive growth solutions that combine global best practices with deep local market expertise.

<div>
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 About Contango
 
 
 
 Contango is the strategic partner for transformative growth and sustained success for the ADQ portfolio. Our team excels in providing comprehensive growth solutions that combine global best practices with local market expertise. We focus on long-term value creation, empowering our clients to achieve the full scale of their aspirations. As a trusted advisor to ADQ's portfolio companies, Contango helps CEOs drive strategic growth initiatives, navigate disruptive forces, and maximize long-term value creation.
 
 
 
 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 &amp; AI solutions. You will translate complex business challenges into scalable, production-ready analytics and machine learning systems, collaborating closely with product, data engineering, and architecture stakeholders to deliver measurable impact.
 
 
 
 Key Responsibilities
 
 
 
 
 Use Case Framing &amp; Solution Design
 
 
 
 <ul>
 <li>
 Translate client business problems into end-to-end system architectures that combine Data, ML, and software components.
 </li>
 <li>
 Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.
 </li>
 <li>
 Make explicit trade-offs across performance, cost, latency, and maintainability.
 </li>
 <li>
 Define success metrics, SLAs, and non-functional requirements (reliability, security, scalability).
 
 
 </li>
 </ul>
 
 Data Engineering &amp; Feature Systems
 
 
 
 <ul>
 <li>
 Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.
 </li>
 <li>
 Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.
 </li>
 <li>
 Collaborate with platform teams to define data models, schemas, and storage strategies.
 </li>
 <li>
 Enforce standards for data validation, testing, and monitoring within production systems.
 
 
 </li>
 </ul>
 
 Applied ML &amp; Production-Grade Development
 
 
 
 <ul>
 <li>
 Develop ML solutions using production-quality code (Python/JS), following software engineering best practices.
 </li>
 <li>
 Structure codebases into maintainable, testable modules, with clear separation of concerns.
 </li>
 <li>
 Implement unit, integration, and end-to-end tests for data and ML components.
 </li>
 <li>
 Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.
 </li>
 <li>
 Balance model sophistication with system performance, latency, and operational constraints.
 
 
 </li>
 </ul>
 
 MLOps, DevOps &amp; Platform Integration
 
 
 
 <ul>
 <li>
 Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.
 </li>
 <li>
 Containerize and deploy services using Docker, Kubernetes, and cloud-native tooling.
 </li>
 <li>
 Implement model versioning, experiment tracking, and artifact management.
 </li>
 <li>
 Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance.
 </li>
 <li>
 Automate retraining, rollback, and release strategies to ensure system resilience.
 
 
 </li>
 </ul>
 
 System Reliability, Scalability &amp; Security
 
 
 
 <ul>
 <li>
 Design systems for high availability, fault tolerance, and horizontal scalability.
 </li>
 <li>
 Optimize performance across data pipelines and inference services (latency, throughput, cost).
 </li>
 <li>
 Apply secure coding practices, access controls, and data protection standards.
 </li>
 <li>
 Manage technical debt and ensure long-term maintainability of production systems.
 
 
 </li>
 </ul>
 
 Documentation, Standards &amp; Engineering Excellence
 
 
 
 <ul>
 <li>
 Produce developer-focused documentation (APIs, architecture diagrams, runbooks).
 </li>
 <li>
 Establish and enforce coding standards, review processes, and engineering best practices.
 </li>
 <li>
 Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.
 </li>
 <li>
 Drive continuous improvement in engineering maturity, tooling, and delivery practices across the consultancy.
 
 
 </li>
 </ul>
 
 Required Experience And Qualifications
 
 
 
 <ul>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Client-serving experience is an advantage.
 </li>
 <li>
 5+ years of experience in data science or a related analytical field.
 </li>
 <li>
 5+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production.
 </li>
 <li>
 Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security.
 </li>
 <li>
 Prior experience in developing AI solutions on public cloud services is an advantage.
 </li>
 <li>
 Bachelor&rsquo;s degree (Master&rsquo;s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering).
 </li>
 <li>
 Technical Expertise:
 </li>
 <ul>
 <li>
 Coding &amp; data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.
 </li>
 <li>
 Statistics &amp; experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.
 </li>
 <li>
 ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised).
 </li>
 <li>
 Data prep &amp; analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.
 </li>
 </ul>
 </ul>
</div>

Senior Data Scientist (6 months contract)

<div>Contract duration: 6 months (with a potential extension) 
Engagement Type: Full-time 
Start date: July 2026 
Location: Abu Dhabi (on-site) 
About Contango:
Contango is the strategic partner for transformative growth and sustained success for the ADQ portfolio. Our team excels in providing comprehensive growth solutions that combine global best practices with local market expertise. We focus on long-term value creation, empowering our clients to achieve the full scale of their aspirations. As a trusted advisor to ADQ's portfolio companies, Contango helps CEOs drive strategic growth initiatives, navigate disruptive forces, and maximize long-term value creation.
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 &amp; AI solutions. You will translate complex business challenges into scalable, production-ready analytics and machine learning systems, collaborating closely with product, data engineering, and architecture stakeholders to deliver measurable impact. 
Key Responsibilities
Use Case Framing &amp; Solution Design<ul><li>
Translate client business problems into end-to-end system architectures that combine Data, ML, and software components.</li><li>
Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.</li><li>
Make explicit trade-offs across performance, cost, latency, and maintainability.</li><li>
Define success metrics, SLAs, and non-functional requirements (reliability, security, scalability).</li></ul>
Data Engineering &amp; Feature Systems<ul><li>
Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.</li><li>
Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.</li><li>
Collaborate with platform teams to define data models, schemas, and storage strategies.</li><li>
Enforce standards for data validation, testing, and monitoring within production systems.</li></ul>
Applied ML &amp; Production-Grade Development<ul><li>
Develop ML solutions using production-quality code (Python/JS), following software engineering best practices.</li><li>
Structure codebases into maintainable, testable modules, with clear separation of concerns.</li><li>
Implement unit, integration, and end-to-end tests for data and ML components.</li><li>
Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.</li><li>
Balance model sophistication with system performance, latency, and operational constraints.</li></ul>
MLOps, DevOps &amp; Platform Integration<ul><li>
Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.</li><li>
Containerize and deploy services using Docker, Kubernetes, and cloud-native tooling.</li><li>
Implement model versioning, experiment tracking, and artifact management.</li><li>
Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance.</li><li>
Automate retraining, rollback, and release strategies to ensure system resilience.</li></ul>
System Reliability, Scalability &amp; Security<ul><li>
Design systems for high availability, fault tolerance, and horizontal scalability.</li><li>
Optimize performance across data pipelines and inference services (latency, throughput, cost).</li><li>
Apply secure coding practices, access controls, and data protection standards.</li><li>
Manage technical debt and ensure long-term maintainability of production systems.</li></ul>
Documentation, Standards &amp; Engineering Excellence<ul><li>
Produce developer-focused documentation (APIs, architecture diagrams, runbooks).</li><li>
Establish and enforce coding standards, review processes, and engineering best practices.</li><li>
Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.</li><li>
Drive continuous improvement in engineering maturity, tooling, and delivery practices across the consultancy.</li></ul>
Required Experience and Qualifications<ul><li>
Prior experience at management consulting firms and/or Big Tech is an advantage.</li><li>
Client-serving experience is an advantage.</li><li>
5+ years of experience in data science or a related analytical field.</li><li>
5+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production.</li><li>
Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security.</li><li>
Prior experience in developing AI solutions on public cloud services is an advantage.</li><li>
Bachelor&rsquo;s degree (Master&rsquo;s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering).</li><li>
Technical Expertise:<ul><li>
Coding &amp; data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.</li><li>
Statistics &amp; experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.</li><li>
ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised).</li><li>
Data prep &amp; analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.</li></ul></li></ul></div>

<div>
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 Role Overview
 
 
 
 We are seeking an experienced DevOps Engineer to make software delivery fast, repeatable, and safe. You&rsquo;ll build and operate CI/CD pipelines, infrastructure-as-code, container platforms, and observability &mdash; cutting lead time and toil while improving reliability and security.
 
 
 The discipline has shifted toward platform engineering: building internal developer platforms and golden paths so product teams can self-serve, with GitOps, policy-as-code, and supply-chain security built in. DevSecOps and FinOps are core parts of the job here, not afterthoughts.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and maintain CI/CD pipelines with automated testing, security scanning, and progressive delivery.
 </li>
 <li>
 Author infrastructure as code with Terraform/OpenTofu using reusable modules and clean environment promotion.
 </li>
 <li>
 Build and operate Kubernetes platforms &mdash; Helm/Kustomize packaging, autoscaling, and ingress.
 </li>
 <li>
 Implement GitOps with Argo CD or Flux so the desired state of infrastructure and apps lives in Git.
 </li>
 <li>
 Stand up observability: metrics, logs, traces, dashboards, SLOs, and alerting.
 </li>
 <li>
 Embed DevSecOps: SAST/DAST/SCA scanning, secrets management, image hardening, and software supply-chain security.
 </li>
 <li>
 Build internal developer platforms and golden paths that enable self-service for product teams.
 </li>
 <li>
 Manage cloud infrastructure with cost awareness, automate operational tasks, and improve DORA metrics; participate in on-call and run blameless post-mortems.
 
 
 </li>
 </ul>
 
 Candidate Requirements
 
 
 
 <ul>
 <li>
 Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.
 </li>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Bachelor's or Master&rsquo;s degree in Computer Science, Engineering, Information Technology, or a related field.
 </li>
 <li>
 3+ years in DevOps, SRE, platform, or infrastructure engineering.
 </li>
 <li>
 Strong Linux fundamentals and scripting in Bash and Python (Go a plus).
 </li>
 <li>
 Production experience with a major cloud (AWS, Azure, or GCP).
 </li>
 <li>
 Hands-on experience running Docker and Kubernetes in production.
 </li>
 <li>
 Proficiency with infrastructure as code (Terraform/OpenTofu) and a CI/CD system (GitHub Actions, GitLab CI, or Jenkins).
 </li>
 <li>
 Solid grasp of networking, DNS, TLS, load balancing, and security fundamentals.
 </li>
 <li>
 Experience with monitoring/observability tooling and incident management.
 
 
 </li>
 </ul>
 
 Nice To Have
 
 
 
 <ul>
 <li>
 Operating ML/AI infrastructure (GPU clusters, model serving, MLOps/LLMOps pipelines).
 </li>
 <li>
 Multi-cloud or hybrid-cloud architecture and migrations.
 </li>
 <li>
 Chaos engineering and advanced reliability practices.
 </li>
 <li>
 eBPF-based tooling (Cilium, Pixie) and custom Kubernetes operators.
 </li>
 <li>
 Certifications such as CKA/CKAD, a cloud architect cert, or Terraform Associate.
 
 
 </li>
 </ul>
 
 Tools &amp; Technologies You&rsquo;ll Work With
 
 
 
 <ul>
 <li>
 Scripting: Bash, Python, Go; YAML, HCL.
 </li>
 <li>
 CI/CD: GitHub Actions, GitLab CI, Jenkins, Azure DevOps.
 </li>
 <li>
 IaC &amp; config: Terraform/OpenTofu, Pulumi, Ansible, Crossplane.
 </li>
 <li>
 Containers &amp; GitOps: Docker, Kubernetes, Helm, Kustomize, Argo CD, Flux, Argo Rollouts.
 </li>
 <li>
 Observability: Prometheus, Grafana, OpenTelemetry, Loki, ELK/OpenSearch, Datadog.
 </li>
 <li>
 DevSecOps: Trivy, Snyk, SonarQube, Checkov, OPA/Kyverno; Sigstore, SLSA, SBOMs.
 </li>
 <li>
 Secrets &amp; platform: HashiCorp Vault, External Secrets, OIDC workload identity; Backstage; AWS, Azure, GCP.
 </li>
 </ul>
</div>

DevOps Engineer (6 months contract)

Join Contango and explore 3   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

Contango

About Company

Senior Data Scientist (6 months contract)

Senior Data Scientist (6 months contract)

DevOps Engineer (6 months contract)