Experienced and results-driven AI/ML Engineer with a proven track record in deploying data science projects and creating infrastructures for data scientists using Python and public cloud platforms, such as Azure. Skilled in applying machine learning techniques to transform data into actionable insights and strategic recommendations. Proficient in machine learning, cloud computing, statistical modelling, data analysis, and data visualisation using tools such as Python, R, SQL, Azure, AWS, GCP, Power BI, and Tableau. Possess excellent communication skills with a strong focus on client engagement, adept at translating technical insights into actionable strategies that deliver tangible results. Excel in leading cross-functional teams and managing multiple projects concurrently to deliver data-driven solutions that meet stakeholder needs and requirements.
– Mentor junior data scientists and machine learning engineers to enhance their problem-solving, coding, and presentation skills.
– Define, test, and implement tools for data science project development and deployment.
– Ensure that the solutions developed by junior data scientists are aligned with client requirements.
– Create proposals to sell projects and present project results to clients’ managers and directors.
– Interview potential candidates, evaluating their skills and suitability for data scientist roles within the company.
– Managed and led a team of 2 Machine Learning Engineers on a company-wide project to create a data warehouse using Azure Synapse, reducing the time needed to access data by 80%.
– Created a tool using Python and Flake8 to validate company code standards, reducing the time needed to validate code standards from 2 hours to 5 minutes.
– Led a team of 2 junior data scientists on a heat prediction project using regression techniques in Python for a multi-billion-dollar steel producer, with an estimated increase in
revenue of over AUD 4 million.
– Applied software development standards and agile methodologies, reducing the project development time by 5%.
– Managed a company-wide project to establish standardised best practices for data science project development, facilitating the process for rotations between projects, resulting
in a 15% improvement in project efficiency.
– Led a team of 3 cloud engineers and 1 machine learning engineer for the implementation of the internal cloud infrastructure. Defined standards (such as name convention) and
instituted industry best practices, such as
– Infrastructure as Code (IaC), reducing project configuration from 6 hours to 30 minutes and cloud costs by 10% through strategic partnerships.
– Led a team of 3 junior analysts, to create and conduct comprehensive training sessions for 15 data scientists on Git, Docker, Azure Machine Learning, and Kedro, improving their
proficiency and productivity.
– Coordinate the creation of the MLOps practice within the organisation, leading the definition of products, roles, responsibilities, and its implementation roadmap.
– Define code development standards for data scientists, ensuring consistency, efficiency, and best practices.
– Create, validate and present detailed progress reports and project deliveries to key stakeholders.
– Develop business proposals and provide technical expertise to enhance the quality and competitiveness of project bids.
– Interview potential candidates, evaluating their skills and suitability for roles within the company
– Designed and implemented the MLOps framework and structure for a multibillion-dollar oil company, impacting 3 data science teams and reducing project deployment time from 6 weeks
to 8 hours.
– Defined and validated best practices for data science project development, used by 20+ data scientists in different teams of the client, resulting in a reduction of 10% in
project development.
– Reduced cloud costs by AUD 50K through best practices and resource deployment optimisation without compromising performance.
– Explore, evaluate, and deploy tools and frameworks to develop and deploy data science projects.
– Facilitated and coordinated meetings with pivotal partners such as Azure and Databricks, fostering a deeper understanding of solutions and strengthening partnerships.
– Demonstrate stakeholder management skills and create compelling presentations showcasing project development and results.
– Mentor junior data scientists across the organisation, promoting a more data-driven culture through LATAM.
– Present project findings and deliveries to managers and directors, effectively communicating complex technical concepts to non-technical stakeholders.
– Help to structure the LATAM data science practice by implementing rituals such as 360 feedback, enhancing team collaboration, and developing a standard project template for data
science projects
– Developed a recommendation algorithm using R, aiding the sales team in selling new items to regular customers, contributing to an estimated AUD 1MM increase in sales revenue, for
a single region.
– Created a customised Power BI dashboard using Alteryx, SQL, and Python for data extraction, transformation and load, resulting in a 90% reduction in analysis time.
– Identified a potential AUD 2MM saving through data analysis of SKU segmentation and warehouse reports using Excel, Python and Alteryx.
– Improved the accuracy of an accident prediction model by 10% in collaboration with the EHS team, engineering a user-friendly web application using Streamlet and Azure,
facilitating model usage by 90%.
– Led training sessions for 150+ employees in the “DNA Academy,” a data and analytics culture dissemination methodology developed by the central team in France.
– Mentored 4 junior data analysts, leading to successful project delivery with potential savings of AUD 50K for the company.
– Conducted training in finance, agile methodologies, design thinking, negotiation, and people management, contributing to a 20% improvement in project delivery timelines and a 50%
enhancement in team management.
– Propose and implement innovative solutions, demonstrate problem-solving skills, the ability to think critically and adaptability to change with project requirements.
– Train and mentor data analysts and consultants on the effective use of Alteryx and Tableau, improving proficiency and accelerating project delivery.
– Collaborate with stakeholders to prioritise tasks and align expectations, ensuring seamless project management.
– Consumed Google Maps APIs using Python to automate the creation of distance matrices and obtain geolocation information, reducing analysis time by more than 80% and increasing
accuracy.
– Developed a Python simulator, achieving a 5% optimisation in the client workforce allocation.
– Built over 10 web crawlers using Python’s libraries Selenium and Beautiful Soup, significantly improving team productivity.
– Collaborated in developing an in-depth optimisation algorithm using Anteryx, resulting in substantial cost savings of more than AUD 100K through enhanced workforce allocation.
– Created dashboards with Tableau using Alteryx for data extraction and transformation of public datasets, enabling complex data analysis and processing in less than 10 minutes.