Machine Learning Lead

Collinson

Collinson is a global loyalty and benefits company.

We use our expertise and products to craft customer experiences which enable some of the world’s best-known brands to acquire, engage and retain the most demanding and choice-rich customers. In particular, our unique expertise and insight into high earning, frequent travellers allows us to create products and solutions for our clients that inspire greater customer engagement to drive more profitable relationships, enrich their travel experiences, protect what matters and assist in in times of need.

While specialising in Financial Services, Travel and Retail, we also support clients in multiple sectors. We have worked with over 90 airlines, 20 hotel groups and more than 600 financial institutions and banks, with clients including Accor Hotels, Air France KLM, American Express, British Airways, Cathay Pacific, Diners Club, Mandarin Oriental, Mastercard, Radisson Hotel Group, Sephora, Visa and VHI.

We take our 30 years’ experience working with these kinds of household names in over 170 countries and help our clients to deliver the smarter experiences it takes to differentiate their propositions and help them win deeper devotion with their customers.

Collinson is a privately-owned entrepreneurial business with 2,000 passionate people working in 20 locations worldwide. Our solutions include Priority Pass, the world’s best known airport experiences programme, while we are also the trusted partner behind many of the leading financial services, airline and hotel brand’s reward programmes and loyalty initiatives.

Purpose of the job

As the Lead ML Engineer, you will serve as the primary point of leadership for the ML platform team, guiding all aspects of the design, development, and delivery of innovative data science and machine learning products. Your responsibilities will encompass the full spectrum of data product creation, from defining project objectives and acquiring data, to exploring and pre-processing data, creating features, experimenting with algorithms, deploying models, and continuously refining solutions through iteration. You will also be tasked with building and maintaining the tools and processes required to support this work.

Also, you will play a critical role in driving the development of 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.

This role requires a deep technical expertise in machine learning and data engineering, as well as excellent leadership and collaboration skills. You must be able to communicate complex technical concepts to both technical and non-technical audiences and have a proven track record of delivering data products that meet business requirements.

Key Responsibilities

Design, Develop and Deliver

• Mentor and manage a team of Machine Learning Engineers, providing guidance, support, and feedback to help them grow and develop professionally.
• Build a centralized ML platform to productionize machine learning workflows, providing ML expertise across Collinson's global footprint.
• Collaborate with the data platform team to integrate with the ML platform and support the growth and development of the team's ML skillset.
• 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.
• Continuously explore and evaluate new and innovative engineering frameworks and techniques to bring fresh ideas to the team and enhance the overall offerings of the Collinson group.
• Collaborate with the data platform team to integrate with the ML platform and support the growth and development of the team's ML skillset.
• Identify and resolve technical issues in a timely and effective manner, ensuring the quality and quantity of work produced by the team is maximized.
• Communicate effectively with stakeholders, both internal and external, to ensure that the team's work aligns with business goals and expectations, and to provide regular updates on project progress.
• 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.

Knowledge, skills and experience required

• In-depth knowledge of data science techniques, including data preparation, exploration, and visualization.
• Expertise in data mining techniques, including statistical modeling methods, time series, text mining, optimization, and information retrieval.
• Ability to develop and implement workflows using various techniques such as classification, clustering, regression, and dimensionality reduction.
• Ability to design and apply statistical analysis and modeling algorithms to solve complex problems in new domains.
• Experience with industry best practices in deploying data science/ML solutions.
• Strong understanding of the AWS Well Architected Framework.
• Core Competencies:
• Proficiency in data science and engineering technologies such as PySpark, PySpark ML, Python, Hive, Postgres, and Sk-learn.
• Strong background in machine learning, including collaborative filtering, NLP, TF-IDF, decision trees, regression, and clustering.
• Data science and analytical expertise.
• Excellent communication skills, with the ability to interact effectively with both technical and non-technical stakeholders.
• Proficiency in machine learning and exploratory data analysis.

Desired Technical Skills

• Experience in using Kubernetes or similar orchestration systems.
• In-depth knowledge of relational database systems such as Oracle, MySQL, and MS SQLServer.
• Exposure to messaging systems such as Kafka.
• Knowledge of distributed computing frameworks like Hadoop and Spark is a plus.
• Expertise in deep learning and artificial intelligence, including MLP, CNN, DCNN, RNN, R-CNN, and GANS.
• Familiarity with cloud providers, with a focus on AWS and Azure.
• Proficiency in visualization and UI technologies, such as Tableau, Plotly, Python Flask, and Zeppelin.

Experience

• Hands-on experience in building and deploying APIs.
• Strong experience with cloud providers, specifically AWS and Azure, in both development and production environments.
• Working knowledge of the Big Data Ecosystem, including Hadoop.
• Proven ability to maintain production-grade workflows.
• Experience with the full software development lifecycle, including both Agile and Waterfall project delivery methods.
• Consultative experience in data science, engineering, or machine learning.

Person Specification:

• 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 orientated
• Thoroughness and attention to detail
• Good communication skills (ability to present, inform and guide others)
• Ability to bridge communications between technical and business focussed groups
• Comfortable working with people at all levels in an organisation
• Willingness to take on a variety of roles and responsibilities