Data Scientist / Applied AI Engineer

Position Overview: We are seeking an Data Scientist / Applied AI Engineer t to turn data into production-ready ML solutions that drive business outcomes. You will work across the ML life cycle data extraction and wrangling, feature engineering, model development and evaluation, and deployment collaborating with product and engineering teams to deliver reliable, well-documented models and data pipelines. The ideal candidate is hands-on with SQL, Python, pandas, polars and has practical Data Science experience.

Key Responsibilities: Collaborate with stakeholders to define analytics questions, success metrics and deliverable timelines. Extract, clean and transform data from relational databases and other sources using SQL, Python, pandas and polars. Perform exploratory data analysis, feature engineering and statistical analysis to inform modeling choices. Design, train and validate machine learning models using scikit-learn (sci-kit learn) and other appropriate libraries, ensuring robust model evaluation and selection. Implement A/B tests and model evaluation procedures to measure business impact and iterate on models. Productionize models and data pipelines: containerize, deploy and automate inference and monitoring workflows in collaboration with engineering teams. Build reproducible, well-documented code, unit tests and versioned experiments; follow software engineering best practices and CI/CD for ML.Create clear data visualizations and reports to communicate insights and model behavior to technical and non-technical stakeholders. Monitor model performance in production, troubleshoot data and model drift, and implement retraining/maintenance strategies.

Qualifications: Bachelors or Masters degree in Computer Science, Statistics, Data Science, Engineering or related field, or equivalent practical experience. Proven hands-on experience with Python and data libraries (pandas, polars) and strong SQL skills for data extraction and transformation. Practical Data Science experience: data exploration, feature engineering, model training, and evaluation. Experience with sci-kit learn (scikit-learn) for building and validating traditional ML models. Solid understanding of statistical methods, metrics and experiment design (A/B testing).Ability to write clean, production-ready code, use version control (Git) and follow software engineering practices for reproducible pipelines. Strong problem-solving skills, attention to detail, and the ability to communicate complex results clearly to stakeholders.

Nice-to-have: deep learning experience, broader machine learning expertise, advanced data wrangling, feature engineering at scale, model deployment experience, model evaluation and monitoring, A/B testing implementation, and data visualization tools such as Seaborn and other plotting libraries.

Paid Health Benefits: (Medical Dental Vision)

Equity: We're an earlier stage, well funded and profitable startup company Our equity package is both fair + compelling and has a 4 year vest Fully Remote Work Environment