Data Scientist
Job Details
Hiring Process
Time to Answer
2 open days
Process
1 Phone Call
1 Onsite Interview
Days to get an Offer
4 Days after Interview
Overview
We are looking for a Data Scientist to design, deploy, and maintain reliable machine learning solutions across cloud and on-premise environments. The successful candidate will combine expertise in machine learning, data engineering, cloud infrastructure, DevOps, and business and technical analysis.
You will work closely with data scientists, data engineers, and business stakeholders to translate requirements into scalable technical solutions. The role also involves implementing MLOps best practices, improving operational workflows, supporting incident resolution, and contributing to FinOps initiatives.
Job Responsibilities
- Deploy machine learning models, specifically using AWS solutions.
- Monitor and maintain the performance and scalability of deployed models, in both cloud and on-premise environments.
- Implement best practices for version control, model tracking, and model lifecycle management.
- Design and manage scalable, reliable, and secure cloud and on-premise infrastructure for machine learning projects.
- Ensure seamless integration between different infrastructure components.
- Implement and maintain CI/CD pipelines for machine learning projects.
- Adopt sound Infrastructure as Code (IaC) principles to ensure consistency and repeatability, enhancing data-driven workflows.
- Work closely with data scientists, data engineers, and other stakeholders to understand project requirements and deliver optimal solutions.
- Stay up-to-date with the latest developments in machine learning, cloud technologies, and DevOps and MLOps practices.
- Identify and implement improvements to existing workflows and systems, including FinOps.
- Participate in incident response activities, especially those related to data integrity and service availability, to help teams dig into root cause analysis.
- Help troubleshoot and resolve performance or data
quality-related issues promptly.
Model Development and Deployment
Infrastructure Management
DevOps Integration
Collaboration and Communication
Continuous Improvement
Incident Response and Troubleshooting
Must Have Skills
- Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, or a related field.
- Proven experience as a Data Scientist, Machine Learning Engineer, Data Engineer, or in a similar role.
- Experience conducting both business and technical analysis.
- Strong understanding of system architecture, with the ability to translate business requirements into scalable technical designs and solutions.
- Experience performing data analysis using Python through scripting and Jupyter Notebooks.
- Strong understanding of MLOps principles, including model deployment, monitoring, and lifecycle management.
- Hands-on experience with both cloud-based and on-premises infrastructure.
- Proficiency in Python and its data science ecosystem.
- Experience with DevOps practices and tools, including:
- CI/CD pipelines.
- Containerization using Docker.
- Orchestration platforms such as Kubernetes or Amazon ECS.
- Strong knowledge of SQL and familiarity with NoSQL databases.
- Excellent analytical and problem-solving skills, with the ability to think critically and creatively.
- Strong communication and stakeholder-management skills, with the ability to collaborate effectively across technical and business teams.
- Ability to work independently, prioritize effectively, and manage multiple tasks in a fast-paced environment.
- Professional working proficiency in Dutch and English.
Nice to have
- Experience with Amazon SageMaker and other AWS services.
- Knowledge of modern data-engineering practices and frameworks, such as dbt and/or Dagster.
- Familiarity with additional programming languages, such as R, Java, or C++.
- Experience with Infrastructure as Code tools and practices.
- Knowledge of model tracking, model registries, automated retraining, and model-monitoring solutions.
- Experience with FinOps, cloud-cost optimization, and performance optimization for machine learning workloads. AWS MLOps practices commonly include model training, deployment, monitoring, versioning, and lifecycle management at scale.
What's great in the job?
- Great team of smart people, in a friendly and open culture
- Expand your knowledge of various business industries
- Create content that will help our users on a daily basis
- Real responsibilities and challenges in a fast evolving company
Work at yechte
We are an independent digital consultancy with ambitious goals and a global presence. We support a diverse range of companies, building digital teams and delivering innovative digital solutions. Our multicultural and diverse workforce, comprised of ‘Global Citizens’, reflects this inclusivity.
We care about work-life balance and meeting the expectation of a growing team, investing in people because they are our greatest asset. Our consistent growth is a testament to this commitment.
Come work at yechte, a company on the rise, offering excellent benefits, opportunities for personal development, and the chance to learn from accomplished leaders. We are always looking for exceptional professionals to join our team.
What We Offer
Each employee has a chance to see the impact of his work. You work on real digital projects and make tangible contributions to the company. We want to provide to each individual personal, professional and social growth.
Flexibility
We care about your wellbeing. At yechte we offer flexi-hours and hybrid home/office work arrangements, enhancing employee work-life balance and productivity.
Attractive Benefits
We care about your comfort. At yechte we offer cost-effective and eco-friendly mobility plans, food allowances, and comprehensive healthcare support, enhancing employee satisfaction.
Personal Development
We care about your growth. At yechte we offer to boost your personal growth through tailored IT trainings and certifications, fostering a culture of agility and tech-driven expertise.
