Key Responsibilities
- Design and build predictive models for attrition risk, payroll anomaly detection, and workforce analytics.
- Partner with product and engineering to ship data-driven features into the core product.
- Clean, transform, and validate large datasets from multiple internal and external sources.
- Communicate findings and model performance clearly to non-technical stakeholders.
- Monitor deployed models for drift and retrain as needed.
- Contribute to the team's data pipeline and feature store infrastructure.
Requirements
- Bachelor's or Master's degree in Computer Science, Statistics, or a related quantitative field.
- 3+ years of experience building and deploying machine learning models in production.
- Strong proficiency in Python, SQL, and common ML frameworks (scikit-learn, PyTorch, or TensorFlow).
- Solid understanding of statistics, experimentation design, and model evaluation.
- Ability to translate ambiguous business problems into well-scoped modeling projects.
Preferred Qualifications
- Experience with HR, payroll, or workforce analytics data.
- Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI).
Benefits & Perks
- Competitive compensation with annual performance appraisal and bonus.
- Comprehensive group health insurance covering employee and immediate family.
- Flexible hybrid work policy with home office allowance.
- Dedicated budget for conferences, courses, and compute resources.
Hiring FAQs
What kind of data will I be working with?
Primarily anonymized attendance, payroll, and engagement data across our customer base, used to power in-product insights.
Is this a research role or product-focused?
Product-focused — models you build ship directly into customer-facing features, not purely research output.
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