[Remote] Principal Data Scientist (AI)
Note: The job is a remote job and is open to candidates in USA. Octave is a company that provides mission-critical software for organizations to make informed decisions across the asset lifecycle. They are seeking a Principal Data Scientist to build predictive models and implement Generative AI features for their compliance management platform, requiring expertise in developing and maintaining 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
Skills
- 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
- 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
- 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
Company Overview