Data Scientist

Summary

Join a groundbreaking team at CoLab Software as a Data Scientist, working on cutting-edge projects in a fast-paced SaaS environment. You'll play a pivotal role in developing and deploying machine learning models, ensuring they're production-ready, scalable, and maintainable.

Requirements

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field
• 3+ years of experience in data science and machine learning, preferably in a SaaS environment
• Strong programming skills in Python (experience with libraries like Pandas, Scikit-Learn, TensorFlow, PyTorch)
• Proficient in SQL and experience with data querying and transformation
• Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (e.g., MLflow, Kubeflow, Docker)
• Familiarity with CI/CD pipelines, version control (Git), and automated testing
• Excellent problem-solving abilities, attention to detail, and the ability to work autonomously in a fast-paced environment
• Strong communication skills with the ability to explain complex concepts to both technical and non-technical audiences

Responsibilities

• Build and Train Models: Design, implement, and deploy machine learning models to drive insights and automate business processes
• Feature Engineering:Develop and optimize features for model training using large, complex datasets
• Experimentation:Lead hypothesis-driven analysis and A/B testing to inform model and product development
• Data Storytelling: Communicate findings through compelling visualizations and presentations, translating data into actionable insights for stakeholders
• Model Deployment and Monitoring: Oversee end-to-end model deployment using MLOps best practices, ensuring models are robust, reproducible, and scalable
• Pipeline Automation:Work with Platform Engineering to develop and maintain automated data pipelines to support continuous integration and deployment (CI/CD) for machine learning workflows
• Model Monitoring and Maintenance: Set up monitoring and alerting for model drift, accuracy, and performance to maintain high-quality predictions in production
• Optimize Infrastructure: Work with engineering and platform teams to optimize cloud infrastructure, model serving, and resource allocation
• Cross-functional Collaboration: Partner with product managers, engineers, and other data scientists to integrate ML solutions into the product and deliver on key business objectives
• Data Governance and Security:Ensure compliance with data privacy and security regulations in all aspects of data processing and model deployment
• Continuous Improvement:Advocate for best practices and contribute to the development of reusable frameworks and processes that accelerate the ML lifecycle

Benefits

• Competitive compensation
• Comprehensive benefits
• Strong commitment to work-life balance