Responsibilities
- Design, train, and deploy machine learning and deep learning models, including propensity models, recommendation engines, and customer behavior prediction systems.
- Own the full ML lifecycle—from feature development through training, evaluation, deployment, and ongoing model monitoring using scalable MLOps pipelines.
- Collaborate with data engineering and business teams to operationalize insights and ML models.
- Design and maintain large-scale ETL/ELT data workflows and integrate structured/unstructured data.
- Develop and integrate with REST and GraphQL APIs for data ingestion and ML-driven services.
- Leverage Python, SQL, Databricks and Apache Spark for data exploration, mining, cleansing and transformation.
- Conduct A/B testing, statistical analysis, and experimentation to improve engagement and business KPIs.
- Implement secure coding practices and leverage Git, CI/CD, and automated testing.
Basic qualifications
- Bachelor’s or Master’s in CS, Data Science, Engineering, Statistics, or related field.
- 7–10 years in data science, ML engineering, or data engineering roles.
- Proficiency in Python, SQL, ML frameworks, and distributed data processing (Spark, Databricks).
- Experience with AWS and Azure.
- Strong ETL/ELT skills and experience with large-scale datasets.
- Experience with REST/GraphQL APIs and third-party API integration.
- Strong understanding of Git, CI/CD, and production-grade ML systems.
Benefits
- Health insurance
- Dental insurance
- Vision insurance
- Long term/short term disability insurance
- Employee assistance program
- Flexible spending account
- Life insurance
- Generous time off policies, including;
- 4-12 weeks fully paid parental leave based on tenure
- 11 paid holidays
- Additional flexible paid vacation and sick leave (US benefits overview)
Tags & focus areas
Used for matching and alerts on DevFound Machine Learning Deep Learning Data Science Mlops Data Engineer Ai