Full-Time Data Scientist

As a Data Scientist, you’ll play a pivotal role in the development and deployment of our machine learning models, working closely with engineering, platform engineering, and product teams. You’ll ensure that our models are not only cutting-edge but also production-ready, scalable, and maintainable. This role is ideal for someone who thrives in a fast-paced SaaS environment and enjoys the blend of data science, engineering, and operational excellence.

What you’ll do: 

• 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.

What you’ll need: 

• 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.
• Willingness to raise your hand when you see something could be done / built better
• Experience working on SaaS, large-scale distributed systems would be considered an asset
•  Consistent track record of building and maintaining highly scalable products would be considered an asset

Success Measured By: 

• Model Accuracy and Impact:Achieving target accuracy and driving measurable business impact.
• Deployment Efficiency:Speed and reliability in moving models to production, with minimal downtime.
• Monitoring and Maintenance:Effective model monitoring with prompt issue detection and resolution.
• Cross-Functional Collaboration:Positive feedback and alignment with product, engineering, and platform teams.
• Process and Innovation Contributions:Development of reusable tools and frameworks to improve the ML lifecycle.