Note: The job is a remote job and is open to candidates in USA. Octave provides mission-critical software that empowers organizations to make informed decisions across every stage of the asset lifecycle. The Lead Data Scientist will build predictive models, implement Generative AI and Agentic AI features, and architect data-driven solutions for their compliance management platform.
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
Benefits
- Remote workplace
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