Senior Data Scientist (Applied ML)
New
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NerdWalletPersonal finance
This role will be remote (based in the U.S.).Full-TimeSenior
Salary$136K - $252K; $136K – $252K • Offers Equity; US: US $136K – $252K • Offers Equity
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Job Details
- Experience
- 5+ years of professional experience in Data Science, Applied Machine Learning, or a related field
- Required Skills
- PythonSQLMLFlowPyTorchTensorflowDatabricksscikit-learn
Requirements
- Have 5+ years of professional experience in Data Science, Applied Machine Learning, or a related field.
- Demonstrate a track record of developing and deploying machine learning solutions with measurable business impact.
- Have hands-on Databricks experience building and managing production ML workflows with MLflow and the Databricks ML ecosystem.
- Be proficient in Python and SQL.
- Have experience with machine learning frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
- Understand supervised learning, predictive modeling, classification, regression, ranking, and model evaluation.
- Have practical experience designing scalable batch and/or real-time inference systems.
- Know software engineering fundamentals including version control, testing, modular code design, and CI/CD for machine learning.
- Be able to lead complex technical initiatives from problem definition through deployment and business impact measurement.
- Connect technical solutions to measurable business outcomes.
- Have a bachelor's or advanced degree in a quantitative discipline, or equivalent practical experience.
Responsibilities
- Lead end-to-end development of machine learning models for customer lifetime value, conversion propensity, lead quality, and revenue optimization.
- Translate model predictions into signals that improve marketing investment decisions and customer acquisition efficiency.
- Develop recommendation and ranking models for personalized financial-product recommendations.
- Collaborate with Product and Marketing to integrate predictive intelligence into consumer experiences and personalized communications.
- Use Databricks and MLflow to develop, train, deploy, monitor, and manage models at scale.
- Establish practices for experiment tracking, model versioning, feature engineering, automated training pipelines, monitoring, and reproducibility.
- Define production standards for model performance, reliability, retraining, and readiness, including data drift and model degradation monitoring.
- Design and evaluate experiments to measure deployed models' incremental business impact.
- Mentor Data Science practitioners and influence architectural and modeling standards.
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