Optimove Machine Learning Engineer 

United Kingdom 

525937295

Best bits of the job:

• Exposure to a phenomenal array of machine learning domains including massive scale search, ranking, NLP, hybridization, classification, and far beyond.
• Fully real-time architecture for data processing and model development and deployment
• Deploying, enhancing ML frameworks, optimizing for inference, and training/retraining
• Online testing for models with live data using proprietary A/B/N testing tech to rapidly figure out what works (and what doesn’t).
• Super-bright, supportive, and friendly machine learning team to work within an environment where rapid experimentation is the norm.
• Friday Foundry - Time each week to research new methods, build and test proofs-of-concept, and deploy to production instantly if effective
• GPU support to efficiently train DL models

Role & Core Responsibilities:

• Own the model development and release process across all products and internal platforms.
• Management of AWS cloud-hosted modeling environment.
• Operationalization of models as APIs working in a real-time environment.
• Own the production monitoring system for models.
• Development of predictive machine learning models for classification and ranking purposes.
• Definition and preparation of new ML applications in close cooperation with product and development teams.
• Analysis of performance and continuous improvement and development of scoring processes hosted models.

Essential requirements:

• 2 years experience in a similar role.
• Experience with AWS environment: Athena, S3, DynamoDB, Batch, CloudWatch Rules & Logs, EventBridge, ECR, or similar with other cloud providers.
• Expert-level knowledge of Python for ML, data manipulation
• Good knowledge of SQL
• Extensive experience with Git, Bash, Docker tools, and machine learning pipelines.
• Experience of working in a real-time analytical environment, and the necessary efficiencies and trade-offs of working in such an environment.
• Experience in the use of open-source machine learning libraries like PyTorch, scipy, and SKLearn along with a good knowledge of NLP.
• Teamwork, communication skills, and hands-on approach.
• Language skills: English.
• Experience performing data analysis to identify opportunities, aid decision-making, and guide model improvements.

Desirable requirements:

• Full understanding of Recommendation algorithms and their applications.
• Understanding and/or direct professional experience of working with less traditional data such as images, video, and text.
• Understanding of Personalising for sports betting and gaming, where it might add value, and what best practice looks like.
• Professional experience in personalization and/or predictive CRM, and micro-segmentation.
• In-depth knowledge of machine learning and statistics for classification and ranking on massive datasets.
• Understanding of Bayesian and/or Frequentist approaches to model comparison / statistical testing.