Lead Data Scientist

What You Get to Do

Technical & Architectural Leadership

• Define and own the ML and advanced analytics architecture supporting HR, benefits, and payroll products.
• Design end-to-end, production-grade ML systems-from data ingestion and feature engineering to model serving, monitoring, and retraining.
• Lead the selection and optimization of algorithms (tree-based models, deep learning, time-

series forecasting, GenAI) with tradeoffs across accuracy, latency, scalability, and cost.

• Establish best practices for model governance, explainability, bias detection, and compliance.

Advanced Modeling & Forecasting

• Drive the development and validation of time-series and forecasting models

(ARIMA/SARIMA, Prophet, state-space models, LSTM/transformers) for workforce planning,

attrition, and financial forecasting.

• Champion advanced experimentation, model evaluation frameworks, and statistical rigor across teams.
• Leverage GenAI/LLMs where appropriate to enhance product intelligence and user experience.

MLOps & Production Excellence

• Partner with DevOps and Platform teams to build robust MLOps pipelines using CI/CD,

automated retraining, monitoring, and alerting.

• Ensure reliable, secure, and scalable model deployment using containerized microservices.
• Define SLAs, performance benchmarks, and operational metrics for ML services in production.

Leadership & Collaboration

• Lead, mentor, and grow a team of senior and mid-level data scientists, fostering a culture of technical excellence and ownership.
• Work closely with Product, Engineering, Security, and Compliance to translate business needs into scalable ML solutions.
• Act as a trusted advisor to stakeholders, influencing product strategy and long-term data science roadmap.

What You Bring

• 12+ years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production
• Familiarity with the setup and use of various open source LLM foundation models.
• Experience with creating and using vectorized databases for data storage and retrieval.
• Familiarity with LLM architecture patterns such as RAG and FLARE.
• Hands on experience with LLM Pretraining, LLM fine-tuning, RLHF, distillation, parameterefficient methods like LoRA, quantization
• Bachelor's degree in Computer Science, Engineering, Mathematics or a related field is required