Job Title:- Data Scientist
Location:- Remote NL
Job Type:- 5+ month contract
Market rate
Job Responsibilities:
Background
The successful candidate will be joining a highly collaborative, cross-functional team, and will be responsible for a structured handover of the current workstreams. In these workstreams, we already have existing GenAI solutions deployed in several markets. The consultant is expected to work on scaling the core solution to expand to more markets, collect feedback from stakeholders and continuously improve our AI systems autonomously based on the feedback.
The scope of the consultant services is to assist:
They will be assigned to work in two main projects, expected to work fully remotely without any travel requirements.
Day-to-day tasks
Day-to-day, the consultant is expected to navigate different PEDX teams, work autonomously towards stakeholder requirements. They will be responsible for monitoring the landscape of emerging AI models and systematically benchmarking them against established performance and latency metrics, keeping an eye on the best GenAI practices and applying them in their work on a day to day basis. They are expected to regularly document their work in detail, communicate effectively with stakeholders.
Requirements:
Desired knowledge, experience, competence, skills etc
We are looking for an excellent communicator with the seniority to work autonomously and translate high-level product requirements into technical execution. The ideal candidate will have 7 (or more) years of experience as a Data Scientist and be comfortable working within Agile frameworks. We require advanced Python (Object Oriented Programming) skills; knowledge of additional programming languages is a plus. Candidates must have a proven track record in full-stack development and GenAI, with specific experience in deploying, scaling, and productionizing AI systems. This requires knowledge of CI/CD for ML (MLOps), containerization (Docker/Kubernetes) and API development.
What 3 things from the box above are most important?
Technical skills: writing maintainable, production-grade code (especially in Python)
Benchmarking and Latency Ops; systematic evaluation for model selection and cost/performance optimization
Strong communication skills and autonomous execution.