Machine Learning Engineer
2 weeks ago
About the Role:
Collinson is a global loyalty and benefits company that uses its expertise and products to craft customer experiences for some of the world's best-known brands. As a Machine Learning Engineer at Collinson, you will play a critical role in driving the development of cloud-based machine learning pipelines for data-driven products and services. Your responsibilities will include collecting data from various business units and leveraging a centralized data platform to productionize analytics and machine learning workflows. Additionally, you will be expected to provide analytical expertise across the Collinson group, ensuring the implementation of cloud-based solutions that meet the needs of both internal and external clients from across Collinson's global footprint.
Key Responsibilities:
The ML Engineer will assist the ML platform team on all aspects of the design, development, and delivery of data science and machine learning products. This role will also focus on all aspects of the design, development, and delivery of data products, including problem definition, data acquisition, data exploration, feature engineering, experimenting with algorithms, machine learning, deploying models, iteratively improving the solution, and building the tools for this process. You will work with data from diverse structured and unstructured data sources in both batch and streaming modes and potentially various formats, including tabular, audio, text, and time series. You are also expected to support the Head of Data Science and Engineering in critical workstreams around Data Science and Engineering.
Requirements:
Knowledge of common data science techniques, including data preparation, exploration, and visualization. Knowledge of data mining techniques in one or more areas of statistical modeling methods, time series, text mining, optimization, and information retrieval. Ability to produce workflows using classification, clustering, regression, and dimensionality reduction. Ability to prototype statistical analysis and modeling algorithms and apply these algorithms for data-driven solutions to problems in new domains. Knowledge of industry best practice in deploying data science solutions. Strong knowledge of the AWS Well-Architected Frameworks. Core competencies include data science and engineering, machine learning, interacting with technical and nontechnical stakeholders, machine learning and exploratory data analysis. Desired technical skills include experience using Kubernetes or similar orchestration systems, in-depth understanding of relational database systems, experience with various messaging systems, experience with distributed computing frameworks, deep learning and artificial intelligence, cloud providers, visualization and UI, and experience with Big Data Ecosystems. Experience with commercial analytical experience, degree in STEM or equivalent, data modeling skills, strong awareness of the appropriate application of denormalization, aggregation, warehousing, and data lakes, experience building and deploying APIs, experience with AWS, Azure, and Big Data Ecosystems, experience maintaining production-grade workflows, experience of the full Software Development Lifecycle, and consultative experience in data science engineering or machine learning. Self-motivator with a desire to learn new skills and embrace new technologies in a constantly changing technology landscape. Ability to thrive in a fast-moving environment. Ability to show initiative, innovation, and work independently when required. Ability to work at pace and tackle project challenges in a collegiate, collaborative way. Goal and outcome-oriented. Thoroughness and attention to detail. Good communication skills, ability to present, inform, and guide others. Ability to bridge communications between technical and business-focused groups. Comfortable working with people at all levels in an organization. Willingness to take on a variety of roles and responsibilities.
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