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Lead Data Scientist
Skills
Computer ScienceApache SparkDatabasesDocument ProcessingDriftISO StandardsSemantic Analysis
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 7+ years in data science ML engineering or related roles
- 3+ years building NLP or generative AI applications and implementing MLOps in production
- Bachelor's or Master's degree in Data Science Computer Science Statistics or related field
- Strong Python experience with Pandas NumPy scikit-learn XGBoost TensorFlow PyTorch Hugging Face Transformers FastAPI Flask MLflow and pytest
- Advanced SQL proficiency with complex queries window functions and optimization
- Strong foundation in supervised and unsupervised learning deep learning document understanding text classification and semantic analysis
- Hands-on experience with foundation models prompt engineering RAG architectures and vector databases
- End-to-end experience with ML pipelines experiment tracking model versioning feature stores drift detection CI/CD for ML and Docker containerization
- Experience with evaluation frameworks custom metrics benchmark datasets and human-in-the-loop validation
- Experience with AWS services including SageMaker Bedrock S3 Lambda EC2 and CloudWatch
- Strong foundation in statistics A/B testing causal inference and experimental design
- Proficiency with Tableau Power BI or Python visualization libraries
- Track record of deploying ML systems processing large-scale datasets with monitoring and governance
- Must be able to work without visa sponsorship
Nice to have
- Agentic AI frameworks
- Life Sciences regulated industries
- Big data tools
- LLM fine-tuning
- ML governance
- Agile environments
- AWS ML certifications
Day to day
- Build predictive models and deploy Generative AI and Agentic AI features for a document-based compliance platform.
- Architect data-driven solutions that automate compliance workflows document review and regulatory mapping with robust production systems.
- Develop end-to-end MLOps pipelines and production Python services while collaborating with cross-functional teams to translate business needs into ML solutions.