Lead Data Scientist

Lead Data Scientist

Octave's ETQ division is seeking a hands-on Data Scientist to build predictive models, implement Generative AI and Agentic AI features, and architect data-driven solutions for our document-based compliance management platform. This role requires a technical expert who can develop, deploy, and maintain ML systems in production environments.

Responsibilities:

• Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and RAG architectures with vector databases for compliance document understanding
• Design agentic AI systems that autonomously handle compliance workflows, document review, regulatory mapping, and multi-step reasoning tasks
• Implement comprehensive LLM evaluation frameworks with automated pipelines, custom metrics, benchmark datasets, and safety guardrails ensuring regulatory compliance
• Build end-to-end MLOps pipelines for model training, deployment, monitoring, versioning, and automated retraining with drift detection
• Develop predictive models for compliance risk scoring, regulatory change impact, anomaly detection, and time-series forecasting
• Write production-quality Python code for data processing, feature engineering, API development (FastAPI/Flask), and ETL/ELT workflows
• Lead A/B experiments and product analytics to measure AI feature impact and drive data-driven decision-making
• Create explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
• Collaborate with cross-functional teams to translate business needs into ML solutions and communicate insights to stakeholders

Python (5+ years): Production-level experience with Pandas, NumPy, scikit-learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest

SQL: Advanced proficiency with complex queries, window functions, and optimization

Machine Learning & NLP: Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis

Generative AI & LLMs: Hands-on experience with foundation models (GPT, Claude, Llama), prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma)

MLOps & ModelOps: End-to-end experience with ML pipelines, experiment tracking (MLflow, W&B), model versioning, feature stores, drift detection, CI/CD for ML, and Docker containerization

LLM Evaluation: Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation

Cloud & AWS: Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch

Statistics & Experimentation: Strong foundation in statistics, A/B testing, causal inference, and experimental design

Visualization: Proficiency with Tableau, Power BI, or Python visualization libraries

Education / Qualifications:

7+ years in data science, ML engineering, or related roles

3+ years building NLP/generative AI applications and implementing MLOps in production

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field (PhD preferred)

Track record of deploying ML systems processing large-scale datasets with proper monitoring and governance

Preferred Qualifications:

Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI)

Knowledge of Life Sciences/regulated industries (FDA, EMA, ISO, GxP) and compliance management systems

Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow), and monitoring tools (Datadog, Prometheus)

Experience with LLM fine-tuning, document processing libraries, multi-modal AI, or distributed training

Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)

Experience working in agile environments with Jira

AWS ML certifications or similar credentials

Key Competencies:

Strong communication skills explaining complex models to technical and non-technical audiences

Ability to work independently and collaboratively in fast-paced environments

Proven ability to convert POCs into production-grade solutions

Understanding of ethical AI and building trustworthy, explainable systems for regulated environments