Machine Learning Engineer

Purpose of the job

As a Machine Learning (ML) 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.

As an innovator, you will be tasked with bringing fresh ideas to the team and continuously exploring new and modern engineering frameworks to enhance the overall offerings of the Collinson group. A key aspect of this role will also be to collaborate with the data platform team to integrate with the ML platform, and to support the growth and development of the team's ML skillset.

Also, it will be essential for you to be able to identify and resolve issues that arise, ensuring the quality and quantity of work produced by the team is maximized at all times. This will require a combination of technical expertise, problem-solving skills, and the ability to effectively communicate and collaborate with stakeholders.

Key Responsibilities

The ML Engineer will assist 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 etc. 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 Head of data science and Engineering in critical workstream around Data Science and Engineering.

Design, Develop and Deliver

• Drive the development of machine learning pipelines for data-driven products and services
• Contribute to architecture and technical decisions to create machine learning workflows and pipelines in cloud (e.g.AWS)
• Collaborate with data scientists and engineers to deploy new machine learning and deep learning models into complex and mission critical production systems
• Select the right tool(s)/services(s) for the job and make it work in production
• Promote a culture of self-serve data analytics by minimizing technical barriers to data access and understanding.
• Stay current with the latest research and technology and communicate your knowledge throughout the enterprise

Day to Day Activities will include

• Working on all stages of projects (planning, development, quality control, production)
• Design, build and ongoing maintenance of our strategic platform and tooling.
• Producing machine learning models including supervised and unsupervised methods
• Rapidly prototyping proof-of-concept idea
• Converting proof-of-concept projects to enterprise solutions
• Producing reports and presentations to communicate findings to stakeholders
• Investigate and understand emerging trends in data-related approaches, performing horizon-scanning that present current and future opportunities for the business.

Team Working

• Be an active member of the data & analytics team, contribute to team dynamics, ways of working and assisting with improvement opportunities
• Be an active member of internal Data and Analytics communities to contribute to team dynamics, ways of working and assisting with improvement opportunities
• Provide regular and accurate reports of progress to Technical leads and the Project lead where required.
• Build strong relationships with stakeholders with a view to providing high-value solutions within the business whilst keeping communication channels open at all times
• Maintain strong technical awareness and familiarity with new and upcoming technologies around Data Integration and Business Intelligence Analysis. Be prepared to give a presentation or provide mentoring of any new technology or skills acquired in a collegiate environment
• Stay abreast of the industry and participate in external communities in order to keep up to date and offer the most informed position when defining or consulting on solution design

Knowledge, skills and experience required

Knowledge

• Knowledge of common data science techniques including data preparation, exploration and visualisation.
• Knowledge of data mining techniques in one or more areas of statistical modelling methods, time series, text mining, optimization, information retrieval.
• Ability to produce workflows using classification, clustering, regression, and dimensionality reduction.
• Ability to prototype statistical analysis and modelling algorithms and apply these algorithms for data driven solutions to problems in new domains.
• Ability to prototype statistical analysis and modelling 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 Framework(s)

Core Competencies

• Data Science and Engineering: PySpark, PySpark ML, Python, Hive, Postgres, Sk-learn
• Machine Learning: Collaborative filtering, NLP, TF-IDF, Decision trees, Regression, Clustering
• Data science and analytical background
• Interacting with technical and non-technical stakeholders
• Machine learning and exploratory data analysis

Desired Technical Skills

• Experience using Kubernetes or similar orchestration systems
• In depth understanding of relational database systems (e.g. Oracle, MySQL, MS SQLServer)
• Experience with various messaging systems, such as Kafka is a plus
• Experience with distributed computing frameworks (e.g. Hadoop, Spark), is a plus

• Deep Learning and artificial Intelligence : MLP, CNN, DCNN, RNN, R-CNN, GANS
• Cloud Providers: AWS (primary), Azure
• Visualization & UI: Tableau, Plotly, Python Flask, Zeppelin
• Experienced in deploying Large Language Models like ChatGPT, Llama etc.

Experience

• 3-4 years commercial analytical experience
• Degree in STEM or equivalent
• Data modelling skills; Strong awareness of the appropriate application of de-normalisation, aggregation, warehousing and data lakes
• Experience building and deploying API’s.
• Experience with AWS, Azure in development and production
• Experience with Big Data Ecosystem (Hadoop)
• Experience maintaining production grade workflows
• Experience of the full Software Development Lifecycle, utilising both Agile and Waterfall Project Delivery methods
• Consultative experience in data science, engineering or machine learning