Machine Learning Engineer
Duration: 6-months (Strong likelihood of a renewal)
Location: Remote for (UK Based)
•Designing and building big data pipelines with machine learning workloads that are repeatable/scalable for extremely large datasets.
•Deploying the latest NLP techniques such as Transformer Models in production.
•Creation of performance metrics and tracking processes to measure the effectiveness of Data Science solutions
•Data governance models to support the technical solution and assurance of the veracity of the data
•Proficiency with programming languages in Big Data platforms, like Python, R, Scala
•Strong knowledge of at least one of the mainstream deep learning frameworks such as PyTorch, TensorFlow
•Understanding software development best practices
•GCP platform: Dataflow, Composer, BigQuery, Vertex AI, or similar techniques in other cloud platforms
•MLOps - MLFlow, Kubeflow, BentoML, or similar
•Productionising machine learning pipelines with Apache Beam and Apache Airflow
•Track record in staying conversant in new analytic technologies, architectures, and languages - where necessary - for storing, processing, and manipulating this type of data
•Demonstrated Data Science consultancy skills, eg running hypotheses workshops, mentoring more junior team members, preparing reports and presenting data science results.
•Skilled to communicate with a variety of stakeholders in the organization
•Planning and organization skills so as to work with a high-performance team, handle demanding clients and multitask effectively and in an agile way
•Team management experience preferred
•5+ years of experience in AI, data science, data engineering, and/or other technology-related capabilities in one or multiple industries. Experience in the Financial Service sector, in particular ESG analytics and risk management, is preferred.
•BSc (ideally MSc or PhD) in Computer Science, Statistics, Engineering, or similar technical field
•Proficient with programming languages like Python, R, Scala,
•Proficient with Git, Linux, Docker
•Software Engineering best practices and Object-Oriented Programming
•Skills in big data technologies like Hadoop, HDFS, Spark, Apache Beam, Apache Airflow
•SQL and NoSQL databases