Lead Machine Learning Scientist
New
United StatesFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- PythonSQLCloud ComputingMachine LearningPyTorchData scienceTensorflowA/B testingscikit-learn
Requirements
- 7+ years of experience in machine learning, data science, or related quantitative fields with a strong record of delivering production-grade ML systems.
- Proven expertise building and scaling marketing-focused machine learning models such as propensity, churn, targeting, personalization, and LTV models.
- Strong proficiency in Python, SQL, and major machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Experience working with large-scale data platforms and complex datasets in cloud-based or modern analytics environments.
- Deep understanding of marketing and growth KPIs including CAC, ROAS, conversion, retention, and monetization metrics.
- Ability to lead highly ambiguous, cross-functional initiatives and translate technical concepts into actionable business strategies.
- Strong communication and stakeholder management skills with experience collaborating across technical and non-technical teams.
- Experience in fintech, financial services, or multi-product ecosystems is highly preferred.
- Familiarity with attribution modeling, marketing measurement, MMM, experimentation frameworks, or Snowflake is a plus.
Responsibilities
- Lead the strategy, development, and deployment of machine learning solutions focused on growth, marketing efficiency, personalization, and customer lifecycle optimization.
- Design and implement high-impact models including propensity scoring, churn prediction, customer lifetime value forecasting, targeting, and cross-sell recommendation systems.
- Partner closely with cross-functional stakeholders in product, marketing, finance, and engineering to align ML initiatives with business objectives and measurable outcomes.
- Drive experimentation and optimization efforts through A/B testing, uplift modeling, and reward/incentive optimization frameworks to improve ROI and customer engagement.
- Identify and integrate high-value internal and external data sources to enhance model performance and support scalable marketing workflows.
- Establish best practices for model development, experimentation, validation, deployment, and monitoring across the organization.
- Promote adoption of modern AI and machine learning techniques, including the use of LLMs and agentic engineering workflows to improve productivity and operational efficiency.
- Lead large-scale, cross-functional ML initiatives while mentoring teams and influencing technical direction across the organization.
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