.With a $150 million funding round received recently from Insight Partners and impressive yearly revenue growth, Lansweeper is rapidly expanding its Global teams. We now need a Senior Platform Engineer to help us scale and to take Lansweeper to the next level.Lansweeper is an IT asset management software helping businesses better understand, manage and protect their IT devices and network. We currently discover and monitor around 100 million connected devices from 25,000+ customers, including Mercedes, FC Barcelona, Michelin, Sony, Carlsberg, Nestlé, IBM, Maersk and Samsung to governments, banks, NGOs, and universities, driven by its 150+ strong teams in Belgium, Spain, Italy, Ireland, UK and the USA.Over 90% of our customers renew their subscriptions every year and we onboard 250 new customers every month. Our high-performance/high-reward culture emphasizes improvement and progress. Under the "work hard, play hard" motto we want employees to grow professionally through ongoing learning and taking on new challenges.If you believe you have what it takes to help us take Lansweeper to the next level, contact us now!Key ResponsibilitiesLeadership & Strategy:Lead the development and execution of complex data science projects from ideation to deployment.Develop and implement data science strategies that align with business objectives and drive impactful outcomes.Mentor and guide junior data scientists, fostering a culture of learning and collaboration.Data Collection & Preparation:Oversee the collection, cleaning, and preprocessing of data from various sources to ensure data quality and integrity.Conduct exploratory data analysis to uncover insights, patterns, and trends.Model Development & Innovation:Design, develop, and implement advanced machine learning models to address complex business challenges.Employ a variety of supervised and unsupervised learning techniques, including deep learning, natural language processing (NLP), and reinforcement learning.Innovate and explore new methodologies to advance our machine learning capabilities.Model Evaluation & Optimization:Evaluate model performance using robust metrics and validation techniques.Optimize models for accuracy, efficiency, scalability, and interpretability.Implement model monitoring and maintenance to ensure long-term effectiveness.Collaboration & Communication:Collaborate with cross-functional teams, including data engineers, software developers, and business stakeholders, to understand requirements and deliver impactful solutions.Communicate complex technical findings and insights to non-technical stakeholders in a clear and concise manner.Research & Continuous Learning:Stay current with the latest advancements in machine learning, data science, and related fields.Lead research initiatives and contribute to the continuous improvement of our data science practices