Job description
Join Lumina Analytics, a leading data scienceādriven product company, as a Senior Data Scientist in San Francisco. You will design and deploy scalable ML models that power strategic decisions across fraud detection, churn prediction, and customer insights.
We are looking for a handsāon data scientist who thrives in a fastāpaced, collaborative environment and can bridge the gap between research and production.
What youāll do:
- Develop, validate, and deploy end-to-end ML/DS solutions from problem framing to production.
- Lead model design with an emphasis on robustness, interpretability, and scalability.
- Collaborate with product, data engineering, and analytics teams to translate business goals into measurable ML outcomes.
- Experiment with feature engineering, model selection, hyperparameter tuning, and evaluation metrics.
- Operationalize models with monitoring, A/B testing, and continued performance optimization.
- Communicate findings through clear data visualizations and storytelling to non-technical stakeholders.
- Mentor junior data scientists and contribute to research and best practices.
- Stay current with industry trends, tools, and methodologies to drive innovation.
Responsibility
- Own end-to-end data science projects from problem definition to deployment and monitoring.
- Design and evaluate ML models with focus on business impact and reliability.
- Collaborate with cross-functional teams to translate requirements into scalable data solutions.
- Build reusable models, pipelines, and documentation for production systems.
- Implement model monitoring, alerting, and ongoing optimization strategies.
- Communicate complex analyses with stakeholders through compelling visuals and narratives.
- Mentor teammates and contribute to a culture of experimentation and learning.
- Contribute to research and development of new algorithms and tools.
Qualification
- Masterās or PhD in Data Science, Computer Science, Statistics, or a related field.
- 5+ years of handsāon experience in data science and ML in production environments.
- Strong programming skills in Python (pandas, scikit-learn, NumPy) and SQL.
- Experience with deep learning frameworks (TensorFlow, PyTorch) and MLOps practices.
- Proven track record in building scalable models and dataādriven products.
- Excellent problemāsolving, communication, and stakeholder management skills.
- Experience with cloud platforms (AWS, GCP) and data processing systems (Spark, Airflow).
- Ability to explain complex models to nonātechnical audiences and influence decisionāmaking.