Machine Learning Engineer
Hang- Full Time
- Senior (5 to 8 years)
Candidates should possess a strong ability to experiment with and explore diverse AI/ML libraries and tools, 3+ years of experience in machine learning with hands-on experience building and deploying ML models, comfort working with Python or similar programming languages for data analysis and model development, and an ability to analyze complex datasets, identify patterns, and translate findings into business-relevant insights. A Master’s degree in Machine Learning or Computer Science (heavily focused on Machine Learning/Artificial Intelligence) is required, along with a desire to learn and experience building and maintaining robust data and machine learning pipelines, including preprocessing, model training, and deployment workflows.
The Machine Learning Engineer will research, prototype, and experiment with various AI/ML libraries and tools to identify the best approaches for analyzing go-to-market data, analyze findings from experiments and provide clear, actionable recommendations on optimal AI/ML methodologies and technologies, collaborate with product and engineering teams to design, develop, and deploy a scalable AI-powered product that delivers insights into go-to-market strategies and their impact on success, build, evaluate, and optimize machine learning models to ensure high performance, accuracy, and scalability, and stay updated on emerging AI/ML trends, tools, and techniques that can be used incorporated into the product.
Automates lead routing for Salesforce users
LeanData automates go-to-market operations to enhance productivity within Salesforce by optimizing lead routing processes. This involves directing potential customer information to the appropriate sales or marketing teams, allowing businesses to follow up on leads more quickly and effectively. LeanData's software solutions enable clients to visualize and adapt their lead management workflows based on data-driven insights. The company's goal is to help sales, marketing, and revenue operations teams work more efficiently, ultimately driving increased revenue.