About Mayflower
Mayflower is a technology company building high-load products used by millions of people worldwide. Its products power real-time entertainment for a global audience.
Responsibilities
•Turn product and business problems into concrete ML implementation plans
•Clarify requirements, constraints, available data, integrations, and success criteria with Product and relevant stakeholders
•Define technical scope, milestones, dependencies, risks, and delivery estimates
•Select appropriate ML approaches and determine reliable ways to validate and implement them quickly
•Drive technical delivery through experimentation, implementation, integration, deployment, and launch readiness
•Keep delivery on track, proactively identify blockers, and coordinate dependencies with other teams
•Provide Product with technical options, trade-offs, estimates, risks, and experiment results for product decisions
•Support production rollout and iteration based on observed results
•Design, train, evaluate, and deploy ML models across domains and problem types
•Write production-quality Python and contribute directly to implementation
•Build APIs, batch jobs, data-processing pipelines, and ML services where appropriate
•Work with classical ML, deep learning, and foundation-model-based approaches depending on the problem
•Process and transform large production datasets using Python and SQL
•Integrate models into existing production systems
•Implement testing, monitoring, logging, and observability for delivered ML solutions
•Work within the shared ML infrastructure, architecture, and engineering practices used across the company
•Collaborate with Data Science, Backend, Data Engineering, and MLOps specialists when deeper expertise or infrastructure changes are required
•Break initiatives into concrete technical tasks and coordinate execution within the stream
•Coordinate the work of Data Scientists and ML Engineers contributing to stream initiatives
•Review technical approaches, experiments, and implementation
•Keep the team focused on agreed scope, priorities, and delivery timelines
•Identify technical risks and dependencies early and drive them to resolution
•Escalate architectural, infrastructure, or methodological questions when broader alignment is required
•Help prepare successful initiatives for scaling or transition to longer-term ownership
•Work closely with Product throughout the delivery lifecycle
•Independently gather the technical details and constraints required to execute product requests
•Communicate estimates, dependencies, technical trade-offs, and delivery status clearly
•Work directly with Engineering and other internal teams to unblock implementation
•Challenge unclear, contradictory, or infeasible requirements and propose practical alternatives
Requirements
•5+ years of commercial experience in Machine Learning, Data Science, or ML Engineering
•Strong hands-on Python programming and software engineering skills
•Experience taking ML solutions from product requirements through experimentation, implementation, integration, and production
•Strong understanding of machine learning methods, statistics, experimentation, and model evaluation
•Experience writing maintainable production code rather than working exclusively in notebooks
•Experience building APIs, services, batch processing, or data pipelines
•Strong SQL skills and experience working with large production datasets
•Practical experience with Docker and production deployment environments
•Ability to turn partially defined problems into concrete technical plans
•Ability to estimate work, identify dependencies and risks, and drive technical execution against a timeline
•Experience owning technical delivery involving several contributors and coordinating work across dependencies
•Experience reviewing code and technical approaches
•Ability to work effectively within established engineering and ML practices while independently owning delivery within a stream
•Strong communication skills and ability to work directly with Product and technical stakeholders
•Ability to balance speed and engineering quality: validate ideas quickly when uncertainty is high and build robust solutions when moving towards production
Nice to have
•Previous experience as a Tech Lead, Stream Lead, or technical owner of ML initiatives
•Kubernetes and CI/CD
•Kafka or other streaming platforms
•Airflow, MLflow, experiment tracking, model monitoring or similar tooling
•Real-time or high-load ML services
•FastAPI or similar Python service frameworks
•Experience across several ML domains such as recommendation systems, ranking, NLP/LLMs, Computer Vision, anomaly detection, forecasting, or classification
•LLM inference, fine-tuning or other GenAI systems
•Experience in teams where Data Scientists and ML Engineers own a substantial part of production implementation themselves
Conditions
•Temporary remote work with future relocation to Limassol
