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