Datatonic

• Provide strategic and technical leadership to the ML team, ensuring alignment with best practices and industry standards. • Recruit, hire, and onboard top talent, including data scientists and machine learning engineers, to strengthen the team. • Offer mentorship and guidance, helping team members develop their technical skills and advance their careers. • Foster a positive, inclusive, and innovative team culture that promotes collaboration and knowledge-sharing. • Conduct regular performance reviews, set clear objectives, and provide constructive feedback to drive continuous improvement. • Oversee and manage complex ML projects from initial concept to successful deployment. • Ensure the delivery of high-quality outputs on time and within budget, maintaining rigorous standards throughout the project lifecycle. • Collaborate with Delivery Managers to define project scope, timelines, resource allocation, and key milestones. • Proactively track project progress, identify potential risks, and implement effective mitigation strategies to keep projects on track. • Build and maintain strong, trusted relationships with clients, gaining a deep understanding of their business objectives and challenges. • Communicate complex technical concepts clearly and effectively to both technical and non-technical client stakeholders. • Present project findings, insights, and actionable recommendations in a compelling and client-friendly manner, ensuring alignment with their goals.

• Interpret vague requirements and develop models to solve real-world problems. • Conduct ML experiments using programming languages with machine learning libraries. • Leverage generative AI to develop innovative solutions. • Optimise machine learning solutions for performance and scalability. • Implement tailored machine learning code to meet specific needs. • Ensure efficient data flow between databases and backend systems. • Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage. • Create machine learning architectures using Google Cloud tools and services. • Build and deploy production-grade software for machine learning and data-driven solutions.

• Lead the execution of small and medium-sized data and AI transformation projects ensuring on-time, on-budget, and high-quality delivery • Proactively work with the project team in linking technical deliverables directly to measurable business outcomes and tracking value realisation post-deployment • Apply and improve appropriate delivery methodologies (Agile, hybrid, Waterfall) suitable for large enterprise environments, acting as a change agent within the client's organisation • Take full financial ownership of the project budget, forecasting resources and revenue, tracking resource burn rate, and driving high margins and profitability • Own account health, proactively identifying and managing key stakeholders, financial forecasts, and strategic risks (RAID management) across the entire portfolio • Manage communications and expectations at the executive and C-suite levels (client-side and internal), maintaining high levels of customer satisfaction and trust. • Contribute to process, operational efficiency, and excellence within the Delivery function, continually challenging how we operate in the efforts of evolving our project management frameworks.

• Build infrastructure that enables analytics and data science teams to deliver innovative, impactful solutions for clients. • Assist clients in migrating their existing business intelligence and data warehouse solutions to Google Cloud. • Design, develop, and optimize robust data pipelines, making data easily accessible for visualization and machine learning applications. • Design and implement new data warehouse and data mart solutions including transforming, testing, deploying, and documenting data. • Architect, maintain, and troubleshoot cloud-based infrastructure to ensure high availability and performance. • Work closely with technology partners such as Google Cloud, Snowflake, dbt, and Looker, mastering their technologies and building a network with their engineers. • Collaborate in an agile and dynamic environment with a team of data engineers, BI analysts, data scientists, and machine learning experts. • Implement software engineering best practices to analytics processes, such as version control, testing, and continuous integration.

• Act as the lead technical authority in high-stakes engagements • Partner with the commercial team to architect winning solutions • Lead the delivery of enterprise-grade systems such as GenAI agents, real-time recommendation engines, or predictive maintenance models • Own the complete technical lifecycle for projects, designing end-to-end ML architectures on GCP and implementing robust MLOps pipelines • Collaborate with the Head of Delivery to define the technical DNA of ML practice, evolving best practices • Spearhead the development of internal accelerators and reusable frameworks • Formally mentor and coach junior and mid-level engineers through code reviews and technical guidance

• Translating Requirements: Interpret vague requirements and develop models to solve real-world problems. • Data Science: Conduct ML experiments using programming languages with machine learning libraries. • GenAI: Leverage generative AI to develop innovative solutions. • Optimisation: Optimise machine learning solutions for performance and scalability. • Custom Code: Implement tailored machine learning code to meet specific needs. • Data Engineering: Ensure efficient data flow between databases and backend systems. • MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage. • ML Architecture Design: Create machine learning architectures using Google Cloud tools and services. • Engineering Software for Production: Build and deploy production-grade software for machine learning and data-driven solutions.

• As an Account Manager, you will take on a hybrid role, balancing the growth of new business opportunities with the expansion and development of existing accounts. • Reporting to the Head of Sales, you will own a big part of the sales strategy and its execution in tandem with other departments (Delivery & Technology). • You will directly contribute to our revenue growth, build strong sales processes, oversee pipelines from qualification to close and manage day-to-day sales operations.