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 systemsfrom 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

Lead Data Scientist