JOB DESCRIPTION

Ready to become a key thought leader and success driver in the development of a cutting-edge SaaS Analytics and Data Science platform? Are you passionate about exciting new applications of Machine Learning and AI?

• Become an industry expert and be passionate about innovating AI/ML applications within it
• Work with technical and non-technical stakeholders to understand, plan, and develop B2B AI/ML products
• Analyze challenges and obstacles, collaborate with diversely skilled team members to find and implement solutions
• Identify areas for improvement with projects and operating procedures

QUALIFICATIONS

• 2+ years’ professional or academic experience in predictive and statistical modeling and machine learning using Python or R
• Strong capabilities in all phases of the modeling pipeline
• Strong data management and coding skills, with ability to write production-level quality code in Python or R, including deep understanding of common Data Science libraries
• Outstanding analytical, quantitative, problem-solving, and critical thinking skills
• Deep theoretical and applied understanding of statistics, data mining, and/or machine learning techniques
• Ability to create/implement monitoring systems to detect data drift, track model versions, report on production model performance
• Proficient in SQL ETL skills, including cleaning, mapping, and transforming data for statistical modeling
• 2+ years of data visualization experience in Tableau or similar dashboarding tool, including creating calculations and telling data stories
• Understanding of best practices in Data Science, Data Visualization and Software Engineering
• Experience using source control tools, such as Github
• Strong written and verbal communication skills, including the ability to present detailed analyses to a broad audience range
• Ability to thrive in a rapidly evolving environment, both independently and within small cross-functional teams
• Self-starter, attention to detail and results orientated, able to work under minimal guidance
• Cloud platforms, such as AWS or Azure (Major Plus)
• CI/CD and DevOps pipelines for automated deployment, including using Docker, Kubernetes, Airflow, Jenkins, or similar tools (Major Plus)
• Linux, Bash and other command line languages (Major Plus)
• SQL Server, Postgres, and MongoDB (Major Plus)
• Logging and error handling libraries for Python or R and building alerts for models (Major Plus)
• Agile and Scrum methodologies (Major Plus)