Lead Data Scientist

Lead Data Scientist

Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA | Roanoke, Virginia-USA Workplace Type: Remote

Responsibilities

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.

Skills

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